Skip to content

F
face
Commit f0ae9c70 by vincent
Options

check in latest build

parent 1c56acb1
Showing with 922 additions and 570 deletions
build/allFacesFactory.d.ts
......@@ -3,10 +3,10 @@ import { TinyYolov2Types } from 'tfjs-tiny-yolov2';
import { TinyYolov2 } from '.';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams } from './mtcnn/types';
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmarkNet, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesSsdMobilenetv1Factory(ssdMobilenetv1: FaceDetectionNet, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, minConfidence?: number, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesTinyYolov2Factory(tinyYolov2: TinyYolov2, landmarkNet: FaceLandmark68Net, recognitionNet: FaceRecognitionNet): (input: TNetInput, forwardParams?: TinyYolov2Types.TinyYolov2ForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
export declare function allFacesMtcnnFactory(mtcnn: Mtcnn, recognitionNet: FaceRecognitionNet): (input: TNetInput, mtcnnForwardParams?: MtcnnForwardParams, useBatchProcessing?: boolean) => Promise<FullFaceDescription[]>;
build/allFacesFactory.js
......@@ -7,24 +7,23 @@ var dom_1 = require("./dom");
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
var alignedFaceCanvases, descriptors, _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, dom_1.extractFaceTensors(input, alignedFaceBoxes)];
case 0: return [4 /*yield*/, dom_1.extractFaces(input, alignedFaceBoxes)];
case 1:
alignedFaceTensors = _b.sent();
alignedFaceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
case 2:
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
descriptors = _a;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, descriptors];
}
});
......@@ -36,27 +35,26 @@ function allFacesFactory(detectFaces, landmarkNet, recognitionNet) {
return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, dom_1.extractFaceTensors(input, detections)];
return [4 /*yield*/, dom_1.extractFaces(input, detections)];
case 2:
faceTensors = _b.sent();
faceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
case 3:
_a = _b.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
case 5:
_a = _b.sent();
_b.label = 6;
case 6:
faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 7:
......
build/allFacesFactory.js.map
{"version":3,"file":"allFacesFactory.js","sourceRoot":"","sources":["../src/allFacesFactory.ts"],"names":[],"mappings":";;;AAAA,2EAAqE;AAMrE,qEAAoE;AACpE,6BAA2C;AAO3C,mCACE,cAAkC;IAElC,OAAO,UAAe,KAAgB,EAAE,gBAAwB,EAAE,kBAA2B;;;;;4BAChE,qBAAM,wBAAkB,CAAC,KAAK,EAAE,gBAAgB,CAAC,EAAA;;wBAAtE,kBAAkB,GAAG,SAAiD;6BAExD,kBAAkB,EAAlB,wBAAkB;wBAClC,qBAAM,cAAc,CAAC,qBAAqB,CAAC,kBAAkB,CAAC,EAAA;;wBAA9D,KAAA,SAAgF,CAAA;;4BAChF,qBAAM,OAAO,CAAC,GAAG,CAAC,kBAAkB,CAAC,GAAG,CACxC,UAAA,UAAU,IAAI,OAAA,cAAc,CAAC,qBAAqB,CAAC,UAAU,CAAC,EAAhD,CAAgD,CAC/D,CAAC,EAAA;;wBAFA,KAAA,SAEkB,CAAA;;;wBAJhB,WAAW,KAIK;wBAEtB,kBAAkB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE5C,sBAAO,WAAW,EAAA;;;;KACnB,CAAA;AACH,CAAC;AAED,yBACE,WAA2D,EAC3D,WAA4B,EAC5B,cAAkC;IAElC,IAAM,kBAAkB,GAAG,yBAAyB,CAAC,cAAc,CAAC,CAAA;IAEpE,OAAO,UACL,KAAgB,EAChB,kBAAmC;QAAnC,mCAAA,EAAA,0BAAmC;;;;;4BAGhB,qBAAM,WAAW,CAAC,KAAK,CAAC,EAAA;;wBAArC,UAAU,GAAG,SAAwB;wBACvB,qBAAM,wBAAkB,CAAC,KAAK,EAAE,UAAU,CAAC,EAAA;;wBAAzD,WAAW,GAAG,SAA2C;6BAEnC,kBAAkB,EAAlB,wBAAkB;wBAC1C,qBAAM,WAAW,CAAC,eAAe,CAAC,WAAW,CAAC,EAAA;;wBAA9C,KAAA,SAAmE,CAAA;;4BACnE,qBAAM,OAAO,CAAC,GAAG,CAAC,WAAW,CAAC,GAAG,CACjC,UAAA,UAAU,IAAI,OAAA,WAAW,CAAC,eAAe,CAAC,UAAU,CAAC,EAAvC,CAAuC,CACtD,CAAC,EAAA;;wBAFA,KAAA,SAEqB,CAAA;;;wBAJnB,mBAAmB,KAIA;wBAEzB,WAAW,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/B,gBAAgB,GAAG,mBAAmB,CAAC,GAAG,CAC9C,UAAC,SAAS,EAAE,CAAC,IAAK,OAAA,SAAS,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,EAAvC,CAAuC,CAC1D,CAAA;wBAEmB,qBAAM,kBAAkB,CAAC,KAAK,EAAE,gBAAgB,EAAE,kBAAkB,CAAC,EAAA;;wBAAnF,WAAW,GAAG,SAAqE;wBAEzF,sBAAO,UAAU,CAAC,GAAG,CAAC,UAAC,SAAS,EAAE,CAAC;gCACjC,OAAA,IAAI,yCAAmB,CACrB,SAAS,EACT,mBAAmB,CAAC,CAAC,CAAC,CAAC,YAAY,CACjC,IAAI,mCAAK,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,EAAE,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,CAC5C,EACD,WAAW,CAAC,CAAC,CAAC,CACf;4BAND,CAMC,CACF,EAAA;;;;KAEF,CAAA;AACH,CAAC;AAED,uCACE,cAAgC,EAChC,WAA4B,EAC5B,cAAkC;IAElC,OAAO,UACL,KAAgB,EAChB,aAA2B,EAC3B,kBAAmC;QADnC,8BAAA,EAAA,mBAA2B;QAC3B,mCAAA,EAAA,0BAAmC;;;;gBAE7B,WAAW,GAAG,UAAC,KAAgB,IAAK,OAAA,cAAc,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,EAAhD,CAAgD,CAAA;gBACpF,QAAQ,GAAG,eAAe,CAAC,WAAW,EAAE,WAAW,EAAE,cAAc,CAAC,CAAA;gBAC1E,sBAAO,QAAQ,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;;KAC3C,CAAA;AACH,CAAC;AAdD,sEAcC;AAED,mCACE,UAAsB,EACtB,WAA4B,EAC5B,cAAkC;IAElC,OAAO,UACL,KAAgB,EAChB,aAA2D,EAC3D,kBAAmC;QADnC,8BAAA,EAAA,kBAA2D;QAC3D,mCAAA,EAAA,0BAAmC;;;;gBAE7B,WAAW,GAAG,UAAC,KAAgB,IAAK,OAAA,UAAU,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,EAA5C,CAA4C,CAAA;gBAChF,QAAQ,GAAG,eAAe,CAAC,WAAW,EAAE,WAAW,EAAE,cAAc,CAAC,CAAA;gBAC1E,sBAAO,QAAQ,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;;KAC3C,CAAA;AACH,CAAC;AAdD,8DAcC;AAED,8BACE,KAAY,EACZ,cAAkC;IAElC,IAAM,kBAAkB,GAAG,yBAAyB,CAAC,cAAc,CAAC,CAAA;IAEpE,OAAO,UACL,KAAgB,EAChB,kBAA2C,EAC3C,kBAAmC;QADnC,mCAAA,EAAA,uBAA2C;QAC3C,mCAAA,EAAA,0BAAmC;;;;;4BAGnB,qBAAM,KAAK,CAAC,OAAO,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;wBAAxD,OAAO,GAAG,SAA8C;wBAExD,gBAAgB,GAAG,OAAO,CAAC,GAAG,CAClC,UAAC,EAAiB;gCAAf,gCAAa;4BAAO,OAAA,aAAa,CAAC,KAAK,EAAE;wBAArB,CAAqB,CAC7C,CAAA;wBAEmB,qBAAM,kBAAkB,CAAC,KAAK,EAAE,gBAAgB,EAAE,kBAAkB,CAAC,EAAA;;wBAAnF,WAAW,GAAG,SAAqE;wBAEzF,sBAAO,OAAO,CAAC,GAAG,CAAC,UAAC,EAAgC,EAAE,CAAC;oCAAjC,gCAAa,EAAE,gCAAa;gCAChD,OAAA,IAAI,yCAAmB,CACrB,aAAa,EACb,aAAa,EACb,WAAW,CAAC,CAAC,CAAC,CACf;4BAJD,CAIC,CACF,EAAA;;;;KAEF,CAAA;AACH,CAAC;AA7BD,oDA6BC"}
\ No newline at end of file
{"version":3,"file":"allFacesFactory.js","sourceRoot":"","sources":["../src/allFacesFactory.ts"],"names":[],"mappings":";;;AAAA,2EAAqE;AAMrE,qEAAoE;AACpE,6BAAqC;AAOrC,mCACE,cAAkC;IAElC,OAAO,UAAe,KAAgB,EAAE,gBAAwB,EAAE,kBAA2B;;;;;4BAC/D,qBAAM,kBAAY,CAAC,KAAK,EAAE,gBAAgB,CAAC,EAAA;;wBAAjE,mBAAmB,GAAG,SAA2C;6BAEnD,kBAAkB,EAAlB,wBAAkB;wBAClC,qBAAM,cAAc,CAAC,qBAAqB,CAAC,mBAAmB,CAAC,EAAA;;wBAA/D,KAAA,SAAiF,CAAA;;4BACjF,qBAAM,OAAO,CAAC,GAAG,CAAC,mBAAmB,CAAC,GAAG,CACzC,UAAA,MAAM,IAAI,OAAA,cAAc,CAAC,qBAAqB,CAAC,MAAM,CAAC,EAA5C,CAA4C,CACvD,CAAC,EAAA;;wBAFA,KAAA,SAEkB,CAAA;;;wBAJhB,WAAW,KAIK;wBAEtB,sBAAO,WAAW,EAAA;;;;KACnB,CAAA;AACH,CAAC;AAED,yBACE,WAA2D,EAC3D,WAA8B,EAC9B,cAAkC;IAElC,IAAM,kBAAkB,GAAG,yBAAyB,CAAC,cAAc,CAAC,CAAA;IAEpE,OAAO,UACL,KAAgB,EAChB,kBAAmC;QAAnC,mCAAA,EAAA,0BAAmC;;;;;4BAGhB,qBAAM,WAAW,CAAC,KAAK,CAAC,EAAA;;wBAArC,UAAU,GAAG,SAAwB;wBACtB,qBAAM,kBAAY,CAAC,KAAK,EAAE,UAAU,CAAC,EAAA;;wBAApD,YAAY,GAAG,SAAqC;6BAE9B,kBAAkB,EAAlB,wBAAkB;wBAC1C,qBAAM,WAAW,CAAC,eAAe,CAAC,YAAY,CAAC,EAAA;;wBAA/C,KAAA,SAAoE,CAAA;;4BACpE,qBAAM,OAAO,CAAC,GAAG,CAAC,YAAY,CAAC,GAAG,CAClC,UAAA,MAAM,IAAI,OAAA,WAAW,CAAC,eAAe,CAAC,MAAM,CAAC,EAAnC,CAAmC,CAC9C,CAAC,EAAA;;wBAFA,KAAA,SAEqB,CAAA;;;wBAJnB,mBAAmB,KAIA;wBAGnB,gBAAgB,GAAG,mBAAmB,CAAC,GAAG,CAC9C,UAAC,SAAS,EAAE,CAAC,IAAK,OAAA,SAAS,CAAC,KAAK,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,EAAvC,CAAuC,CAC1D,CAAA;wBAEmB,qBAAM,kBAAkB,CAAC,KAAK,EAAE,gBAAgB,EAAE,kBAAkB,CAAC,EAAA;;wBAAnF,WAAW,GAAG,SAAqE;wBAEzF,sBAAO,UAAU,CAAC,GAAG,CAAC,UAAC,SAAS,EAAE,CAAC;gCACjC,OAAA,IAAI,yCAAmB,CACrB,SAAS,EACT,mBAAmB,CAAC,CAAC,CAAC,CAAC,YAAY,CACjC,IAAI,mCAAK,CAAC,SAAS,CAAC,GAAG,CAAC,CAAC,EAAE,SAAS,CAAC,GAAG,CAAC,CAAC,CAAC,CAC5C,EACD,WAAW,CAAC,CAAC,CAAC,CACf;4BAND,CAMC,CACF,EAAA;;;;KAEF,CAAA;AACH,CAAC;AAED,uCACE,cAAgC,EAChC,WAA8B,EAC9B,cAAkC;IAElC,OAAO,UACL,KAAgB,EAChB,aAA2B,EAC3B,kBAAmC;QADnC,8BAAA,EAAA,mBAA2B;QAC3B,mCAAA,EAAA,0BAAmC;;;;gBAE7B,WAAW,GAAG,UAAC,KAAgB,IAAK,OAAA,cAAc,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,EAAhD,CAAgD,CAAA;gBACpF,QAAQ,GAAG,eAAe,CAAC,WAAW,EAAE,WAAW,EAAE,cAAc,CAAC,CAAA;gBAC1E,sBAAO,QAAQ,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;;KAC3C,CAAA;AACH,CAAC;AAdD,sEAcC;AAED,mCACE,UAAsB,EACtB,WAA8B,EAC9B,cAAkC;IAElC,OAAO,UACL,KAAgB,EAChB,aAA2D,EAC3D,kBAAmC;QADnC,8BAAA,EAAA,kBAA2D;QAC3D,mCAAA,EAAA,0BAAmC;;;;gBAE7B,WAAW,GAAG,UAAC,KAAgB,IAAK,OAAA,UAAU,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,EAA5C,CAA4C,CAAA;gBAChF,QAAQ,GAAG,eAAe,CAAC,WAAW,EAAE,WAAW,EAAE,cAAc,CAAC,CAAA;gBAC1E,sBAAO,QAAQ,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;;KAC3C,CAAA;AACH,CAAC;AAdD,8DAcC;AAED,8BACE,KAAY,EACZ,cAAkC;IAElC,IAAM,kBAAkB,GAAG,yBAAyB,CAAC,cAAc,CAAC,CAAA;IAEpE,OAAO,UACL,KAAgB,EAChB,kBAA2C,EAC3C,kBAAmC;QADnC,mCAAA,EAAA,uBAA2C;QAC3C,mCAAA,EAAA,0BAAmC;;;;;4BAGnB,qBAAM,KAAK,CAAC,OAAO,CAAC,KAAK,EAAE,kBAAkB,CAAC,EAAA;;wBAAxD,OAAO,GAAG,SAA8C;wBAExD,gBAAgB,GAAG,OAAO,CAAC,GAAG,CAClC,UAAC,EAAiB;gCAAf,gCAAa;4BAAO,OAAA,aAAa,CAAC,KAAK,EAAE;wBAArB,CAAqB,CAC7C,CAAA;wBAEmB,qBAAM,kBAAkB,CAAC,KAAK,EAAE,gBAAgB,EAAE,kBAAkB,CAAC,EAAA;;wBAAnF,WAAW,GAAG,SAAqE;wBAEzF,sBAAO,OAAO,CAAC,GAAG,CAAC,UAAC,EAAgC,EAAE,CAAC;oCAAjC,gCAAa,EAAE,gCAAa;gCAChD,OAAA,IAAI,yCAAmB,CACrB,aAAa,EACb,aAAa,EACb,WAAW,CAAC,CAAC,CAAC,CACf;4BAJD,CAIC,CACF,EAAA;;;;KAEF,CAAA;AACH,CAAC;AA7BD,oDA6BC"}
\ No newline at end of file
build/dom/extractFaceTensors.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { Rect, TNetInput } from 'tfjs-image-recognition-base';
import { Rect } from 'tfjs-image-recognition-base';
import { FaceDetection } from '../classes/FaceDetection';
/**
* Extracts the tensors of the image regions containing the detected faces.
......@@ -7,8 +7,8 @@ import { FaceDetection } from '../classes/FaceDetection';
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param input The image that face detection has been performed on.
* @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
export declare function extractFaceTensors(input: TNetInput, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor4D[]>;
export declare function extractFaceTensors(imageTensor: tf.Tensor3D | tf.Tensor4D, detections: Array<FaceDetection | Rect>): Promise<tf.Tensor3D[]>;
build/dom/extractFaceTensors.js
......@@ -10,41 +10,28 @@ var FaceDetection_1 = require("../classes/FaceDetection");
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param input The image that face detection has been performed on.
* @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(input, detections) {
function extractFaceTensors(imageTensor, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
if (tfjs_image_recognition_base_1.isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tf.tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var _a = imageTensor.shape.slice(tfjs_image_recognition_base_1.isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return tf.slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
return tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors;
})];
}
});
});
}
......
build/dom/extractFaceTensors.js.map
{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA0E;AAE1E,0DAAyD;AAEzD;;;;;;;;;GASG;AACH,4BACE,KAAgB,EAChB,UAAuC;;;;;wBAGtB,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;oBAAxC,QAAQ,GAAG,SAA6B;oBAE9C,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,IAAI,QAAQ,CAAC,SAAS,EAAE;4BACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;yBACnB;wBACD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;qBACpE;oBAED,sBAAO,EAAE,CAAC,IAAI,CAAC;4BACb,IAAM,SAAS,GAAG,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC,OAAO,EAAiB,CAAA;4BAEpE,IAAA,6BAA6D,EAA5D,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4B;4BAEnE,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;gCACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,MAAM,EAAE;gCAC3C,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;iCACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;4BAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;oCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;gCAClD,OAAA,EAAE,CAAC,OAAO,CAAC,SAAS,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;4BAApE,CAAoE,CACrE,CAAA;4BAED,IAAI,QAAQ,CAAC,SAAS,EAAE;gCACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;6BACnB;4BACD,OAAO,WAAW,CAAA;wBACpB,CAAC,CAAC,EAAA;;;;CACH;AAnCD,gDAmCC"}
\ No newline at end of file
{"version":3,"file":"extractFaceTensors.js","sourceRoot":"","sources":["../../src/dom/extractFaceTensors.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA+D;AAE/D,0DAAyD;AAEzD;;;;;;;;;GASG;AACH,4BACE,WAAsC,EACtC,UAAuC;;;YAGvC,IAAI,wCAAU,CAAC,WAAW,CAAC,IAAI,WAAW,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,EAAE;gBACvD,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;aACpE;YAED,sBAAO,EAAE,CAAC,IAAI,CAAC;oBACP,IAAA,2FAA6F,EAA5F,iBAAS,EAAE,gBAAQ,EAAE,mBAAW,CAA4D;oBAEnG,IAAM,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC,MAAM,EAAE;wBAC3C,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,QAAQ,EAAE,SAAS,CAAC,EAA3C,CAA2C,CAAC,CAAA;oBAE1D,IAAM,WAAW,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;4BAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;wBAClD,OAAA,EAAE,CAAC,OAAO,CAAC,WAAW,CAAC,IAAI,CAAC,SAAS,EAAE,QAAQ,EAAE,WAAW,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,EAAE,WAAW,CAAC,CAAC;oBAAvG,CAAuG,CACxG,CAAA;oBAED,OAAO,WAAW,CAAA;gBACpB,CAAC,CAAC,EAAA;;;CACH;AAzBD,gDAyBC"}
\ No newline at end of file
build/dom/extractFaces.js
......@@ -12,29 +12,30 @@ var FaceDetection_1 = require("../classes/FaceDetection");
*/
function extractFaces(input, detections) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
netInput = _b.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
if (netInput.isManaged) {
netInput.dispose();
}
_a.label = 3;
tensorOrCanvas = netInput.getInput(0);
if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
_a = tensorOrCanvas;
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, tfjs_image_recognition_base_1.imageTensorToCanvas(tensorOrCanvas)];
case 3:
_a = _b.sent();
_b.label = 4;
case 4:
canvas = _a;
_b.label = 5;
case 5:
ctx = tfjs_image_recognition_base_1.getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection_1.FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
......
build/dom/extractFaces.js.map
{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../src/dom/extractFaces.ts"],"names":[],"mappings":";;;AAAA,2EAOqC;AAErC,0DAAyD;AAEzD;;;;;;GAMG;AACH,sBACE,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;oBAAxC,QAAQ,GAAG,SAA6B;oBAE9C,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,IAAI,QAAQ,CAAC,SAAS,EAAE;4BACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;yBACnB;wBACD,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEQ,qBAAM,iDAAmB,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAA;;oBAAtD,MAAM,GAAG,SAA6C,CAAA;oBAEtD,IAAI,QAAQ,CAAC,SAAS,EAAE;wBACtB,QAAQ,CAAC,OAAO,EAAE,CAAA;qBACnB;;;oBAGG,GAAG,GAAG,iDAAmB,CAAC,MAAM,CAAC,CAAA;oBAEjC,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;wBAC3D,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,EAAnD,CAAmD,CAAC,CAAA;oBAElE,sBAAO,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;gCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;4BACrC,IAAM,OAAO,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,iDAAmB,CAAC,OAAO,CAAC;iCACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;4BAC5D,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAC,EAAA;;;;CACH;AAvCD,oCAuCC"}
\ No newline at end of file
{"version":3,"file":"extractFaces.js","sourceRoot":"","sources":["../../src/dom/extractFaces.ts"],"names":[],"mappings":";;;AAAA,2EAOqC;AAErC,0DAAyD;AAEzD;;;;;;GAMG;AACH,sBACE,KAAgB,EAChB,UAAuC;;;;;;oBAGnC,MAAM,GAAG,KAA0B,CAAA;yBAEnC,CAAC,CAAC,KAAK,YAAY,iBAAiB,CAAC,EAArC,wBAAqC;oBACtB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;oBAAlC,QAAQ,GAAG,SAAuB;oBAExC,IAAI,QAAQ,CAAC,SAAS,GAAG,CAAC,EAAE;wBAC1B,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAA;qBAC9D;oBAEK,cAAc,GAAG,QAAQ,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;yBAClC,CAAA,cAAc,YAAY,iBAAiB,CAAA,EAA3C,wBAA2C;oBAChD,KAAA,cAAc,CAAA;;wBACd,qBAAM,iDAAmB,CAAC,cAAc,CAAC,EAAA;;oBAAzC,KAAA,SAAyC,CAAA;;;oBAF7C,MAAM,KAEuC,CAAA;;;oBAGzC,GAAG,GAAG,iDAAmB,CAAC,MAAM,CAAC,CAAA;oBACjC,KAAK,GAAG,UAAU,CAAC,GAAG,CAC1B,UAAA,GAAG,IAAI,OAAA,GAAG,YAAY,6BAAa;wBACjC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC,MAAM,EAAE,CAAC,KAAK,EAAE;wBAC3D,CAAC,CAAC,GAAG,EAFA,CAEA,CACR;yBACE,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,kBAAkB,CAAC,MAAM,CAAC,KAAK,EAAE,MAAM,CAAC,MAAM,CAAC,EAAnD,CAAmD,CAAC,CAAA;oBAElE,sBAAO,KAAK,CAAC,GAAG,CAAC,UAAC,EAAuB;gCAArB,QAAC,EAAE,QAAC,EAAE,gBAAK,EAAE,kBAAM;4BACrC,IAAM,OAAO,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;4BAC/C,iDAAmB,CAAC,OAAO,CAAC;iCACzB,YAAY,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAA;4BAC5D,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAC,EAAA;;;;CACH;AAlCD,oCAkCC"}
\ No newline at end of file
build/faceDetectionNet/FaceDetectionNet.js
......@@ -21,7 +21,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
throw new Error('FaceDetectionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(512, false);
var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1));
var features = mobileNetV1_1.mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer_1.predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
......@@ -35,7 +35,7 @@ var FaceDetectionNet = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -45,10 +45,10 @@ var FaceDetectionNet = /** @class */ (function (_super) {
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return tslib_1.__generator(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
......@@ -64,17 +64,20 @@ var FaceDetectionNet = /** @class */ (function (_super) {
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression_1.nonMaxSuppression(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddings = netInput.getRelativePaddings(0);
reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddings.y; }), top = _a[0], bottom = _a[1];
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection_1.FaceDetection(scoresData[idx], new tfjs_image_recognition_base_1.Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
......
build/faceDetectionNet/FaceDetectionNet.js.map
{"version":3,"file":"FaceDetectionNet.js","sourceRoot":"","sources":["../../src/faceDetectionNet/FaceDetectionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAmG;AAEnG,0DAAyD;AACzD,iDAAgD;AAChD,6DAA4D;AAC5D,6CAA4C;AAC5C,yDAAwD;AACxD,6CAA4C;AAC5C,qDAAoD;AAGpD;IAAsC,4CAAwB;IAE5D;eACE,kBAAM,kBAAkB,CAAC;IAC3B,CAAC;IAEM,uCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;SAClE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,KAAK,CAAC,CAAA;YAEnD,IAAM,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,WAAW,EAAE,EAAE,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAgB,CAAA;YACnG,IAAM,QAAQ,GAAG,yBAAW,CAAC,CAAC,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAE7C,IAAA,8FAGqE,EAFzE,kCAAc,EACd,sCAAgB,CACyD;YAE3E,OAAO,yBAAW,CAAC,cAAc,EAAE,gBAAgB,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3E,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,kCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,sCAAW,GAAxB,UACE,KAAgB,EAChB,aAA2B,EAC3B,UAAwB;QADxB,8BAAA,EAAA,mBAA2B;QAC3B,2BAAA,EAAA,gBAAwB;;;;;4BAGP,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,KAGF,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,EAFtB,MAAM,WAAA,EACL,OAAO,YAAA,CACc;wBAIzB,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;wBACjB,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;wBACzB,KAAS,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;4BACtC,MAAM,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;4BACnB,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;yBACrB;wBAGkB,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;wBAAC,qBAAM,MAAM,CAAC,IAAI,EAAE,EAAA;;wBAA3C,UAAU,GAAG,cAAW,SAAmB,EAAC;wBAE5C,YAAY,GAAG,GAAG,CAAA;wBAClB,OAAO,GAAG,qCAAiB,CAC/B,KAAK,EACL,UAAU,EACV,UAAU,EACV,YAAY,EACZ,aAAa,CACd,CAAA;wBAEK,QAAQ,GAAG,QAAQ,CAAC,mBAAmB,CAAC,CAAC,CAAC,CAAA;wBAE1C,OAAO,GAAG,OAAO;6BACpB,GAAG,CAAC,UAAA,GAAG;4BACA,IAAA;;;8EAGwB,EAHvB,WAAG,EAAE,cAAM,CAGY;4BACxB,IAAA;;;8EAGwB,EAHvB,YAAI,EAAE,aAAK,CAGY;4BAC9B,OAAO,IAAI,6BAAa,CACtB,UAAU,CAAC,GAAG,CAAC,EACf,IAAI,kCAAI,CACN,IAAI,EACJ,GAAG,EACH,KAAK,GAAG,IAAI,EACZ,MAAM,GAAG,GAAG,CACb,EACD;gCACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC;gCAClC,KAAK,EAAE,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC;6BACjC,CACF,CAAA;wBACH,CAAC,CAAC,CAAA;wBAEJ,KAAK,CAAC,OAAO,EAAE,CAAA;wBACf,MAAM,CAAC,OAAO,EAAE,CAAA;wBAEhB,sBAAO,OAAO,EAAA;;;;KACf;IAES,8CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,wCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,uBAAC;AAAD,CAAC,AA3GD,CAAsC,2CAAa,GA2GlD;AA3GY,4CAAgB"}
\ No newline at end of file
{"version":3,"file":"FaceDetectionNet.js","sourceRoot":"","sources":["../../src/faceDetectionNet/FaceDetectionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAmG;AAEnG,0DAAyD;AACzD,iDAAgD;AAChD,6DAA4D;AAC5D,6CAA4C;AAC5C,yDAAwD;AACxD,6CAA4C;AAC5C,qDAAoD;AAGpD;IAAsC,4CAAwB;IAE5D;eACE,kBAAM,kBAAkB,CAAC;IAC3B,CAAC;IAEM,uCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,gDAAgD,CAAC,CAAA;SAClE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,KAAK,CAAC,CAAC,OAAO,EAAE,CAAA;YAE7D,IAAM,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,WAAW,EAAE,EAAE,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAC,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAgB,CAAA;YACnG,IAAM,QAAQ,GAAG,yBAAW,CAAC,CAAC,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAE7C,IAAA,8FAGqE,EAFzE,kCAAc,EACd,sCAAgB,CACyD;YAE3E,OAAO,yBAAW,CAAC,cAAc,EAAE,gBAAgB,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;QAC3E,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,kCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,sCAAW,GAAxB,UACE,KAAgB,EAChB,aAA2B,EAC3B,UAAwB;QADxB,8BAAA,EAAA,mBAA2B;QAC3B,2BAAA,EAAA,gBAAwB;;;;;4BAGP,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAElC,KAGF,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,EAFtB,MAAM,WAAA,EACL,OAAO,YAAA,CACc;wBAIzB,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAA;wBACjB,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,CAAA;wBACzB,KAAS,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;4BACtC,MAAM,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;4BACnB,OAAO,CAAC,CAAC,CAAC,CAAC,OAAO,EAAE,CAAA;yBACrB;wBAGkB,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;wBAAC,qBAAM,MAAM,CAAC,IAAI,EAAE,EAAA;;wBAA3C,UAAU,GAAG,cAAW,SAAmB,EAAC;wBAE5C,YAAY,GAAG,GAAG,CAAA;wBAClB,OAAO,GAAG,qCAAiB,CAC/B,KAAK,EACL,UAAU,EACV,UAAU,EACV,YAAY,EACZ,aAAa,CACd,CAAA;wBAEK,YAAY,GAAG,QAAQ,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAA;wBACrD,SAAS,GAAG,QAAQ,CAAC,SAAmB,CAAA;wBACxC,IAAI,GAAG,SAAS,GAAG,YAAY,CAAC,KAAK,CAAA;wBACrC,IAAI,GAAG,SAAS,GAAG,YAAY,CAAC,MAAM,CAAA;wBAEtC,OAAO,GAAG,OAAO;6BACpB,GAAG,CAAC,UAAA,GAAG;4BACA,IAAA;;;wEAGkB,EAHjB,WAAG,EAAE,cAAM,CAGM;4BAClB,IAAA;;;wEAGkB,EAHjB,YAAI,EAAE,aAAK,CAGM;4BACxB,OAAO,IAAI,6BAAa,CACtB,UAAU,CAAC,GAAG,CAAC,EACf,IAAI,kCAAI,CACN,IAAI,EACJ,GAAG,EACH,KAAK,GAAG,IAAI,EACZ,MAAM,GAAG,GAAG,CACb,EACD;gCACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,CAAC,CAAC;gCAClC,KAAK,EAAE,QAAQ,CAAC,aAAa,CAAC,CAAC,CAAC;6BACjC,CACF,CAAA;wBACH,CAAC,CAAC,CAAA;wBAEJ,KAAK,CAAC,OAAO,EAAE,CAAA;wBACf,MAAM,CAAC,OAAO,EAAE,CAAA;wBAEhB,sBAAO,OAAO,EAAA;;;;KACf;IAES,8CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,wCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,uBAAC;AAAD,CAAC,AA9GD,CAAsC,2CAAa,GA8GlD;AA9GY,4CAAgB"}
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68Net.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput } from 'tfjs-image-recognition-base';
import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
import { NetParams } from './types';
export declare class FaceLandmark68Net extends FaceLandmark68NetBase<NetParams> {
constructor();
runNet(input: NetInput): tf.Tensor2D;
protected loadQuantizedParams(uri: string | undefined): Promise<{
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
}>;
protected extractParams(weights: Float32Array): {
params: NetParams;
paramMappings: {
originalPath?: string | undefined;
paramPath: string;
}[];
};
}
build/faceLandmarkNet/FaceLandmark68Net.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_tiny_yolov2_1 = require("tfjs-tiny-yolov2");
var extractParams_1 = require("./extractParams");
var FaceLandmark68NetBase_1 = require("./FaceLandmark68NetBase");
var fullyConnectedLayer_1 = require("./fullyConnectedLayer");
var loadQuantizedParams_1 = require("./loadQuantizedParams");
function conv(x, params) {
return tfjs_tiny_yolov2_1.convLayer(x, params, 'valid', true);
}
function maxPool(x, strides) {
if (strides === void 0) { strides = [2, 2]; }
return tf.maxPool(x, [2, 2], strides, 'valid');
}
var FaceLandmark68Net = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68Net, _super);
function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this;
}
FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmark68Net - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(128, true).toFloat();
var out = conv(batchTensor, params.conv0);
out = maxPool(out);
out = conv(out, params.conv1);
out = conv(out, params.conv2);
out = maxPool(out);
out = conv(out, params.conv3);
out = conv(out, params.conv4);
out = maxPool(out);
out = conv(out, params.conv5);
out = conv(out, params.conv6);
out = maxPool(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = tf.relu(fullyConnectedLayer_1.fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
return fullyConnectedLayer_1.fullyConnectedLayer(fc0, params.fc1);
});
};
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams_1.loadQuantizedParams(uri);
};
FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams_1.extractParams(weights);
};
return FaceLandmark68Net;
}(FaceLandmark68NetBase_1.FaceLandmark68NetBase));
exports.FaceLandmark68Net = FaceLandmark68Net;
//# sourceMappingURL=FaceLandmark68Net.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68Net.js.map 0 → 100644
{"version":3,"file":"FaceLandmark68Net.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmark68Net.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,qDAAyD;AAEzD,iDAAgD;AAChD,iEAAgE;AAChE,6DAA4D;AAC5D,6DAA4D;AAG5D,cAAc,CAAc,EAAE,MAAkB;IAC9C,OAAO,4BAAS,CAAC,CAAC,EAAE,MAAM,EAAE,OAAO,EAAE,IAAI,CAAC,CAAA;AAC5C,CAAC;AAED,iBAAiB,CAAc,EAAE,OAAkC;IAAlC,wBAAA,EAAA,WAA6B,CAAC,EAAE,CAAC,CAAC;IACjE,OAAO,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;AAChD,CAAC;AAED;IAAuC,6CAAgC;IAErE;eACE,kBAAM,mBAAmB,CAAC;IAC5B,CAAC;IAEM,kCAAM,GAAb,UAAc,KAAe;QAEnB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,iDAAiD,CAAC,CAAA;SACnE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,OAAO,EAAiB,CAAA;YAE3E,IAAI,GAAG,GAAG,IAAI,CAAC,WAAW,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YACzC,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,CAAC,CAAA;YAClB,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,GAAG,GAAG,OAAO,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;YAC1B,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,MAAM,CAAC,KAAK,CAAC,CAAA;YAC7B,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,yCAAmB,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,GAAG,CAAC,CAAC,CAAA;YAEhF,OAAO,yCAAmB,CAAC,GAAG,EAAE,MAAM,CAAC,GAAG,CAAC,CAAA;QAC7C,CAAC,CAAC,CAAA;IACJ,CAAC;IAES,+CAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,yCAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,wBAAC;AAAD,CAAC,AA1CD,CAAuC,6CAAqB,GA0C3D;AA1CY,8CAAiB"}
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68NetBase.d.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { NetInput, NeuralNetwork, TNetInput, Dimensions } from 'tfjs-image-recognition-base';
import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
export declare class FaceLandmark68NetBase<NetParams> extends NeuralNetwork<NetParams> {
private __name;
constructor(_name: string);
runNet(_: NetInput): tf.Tensor2D;
postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: Dimensions[]): tf.Tensor2D;
forwardInput(input: NetInput): tf.Tensor2D;
forward(input: TNetInput): Promise<tf.Tensor2D>;
detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>;
}
build/faceLandmarkNet/FaceLandmark68NetBase.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var FaceLandmarks68_1 = require("../classes/FaceLandmarks68");
var FaceLandmark68NetBase = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmark68NetBase, _super);
function FaceLandmark68NetBase(_name) {
var _this = _super.call(this, _name) || this;
_this.__name = _name;
return _this;
}
FaceLandmark68NetBase.prototype.runNet = function (_) {
throw new Error(this.__name + " - runNet not implemented");
};
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
var inputDimensions = originalDimensions.map(function (_a) {
var width = _a.width, height = _a.height;
var scale = inputSize / Math.max(height, width);
return {
width: width * scale,
height: height * scale
};
});
var batchSize = inputDimensions.length;
return tf.tidy(function () {
var createInterleavedTensor = function (fillX, fillY) {
return tf.stack([
tf.fill([68], fillX),
tf.fill([68], fillY)
], 1).as2D(1, 136).as1D();
};
var getPadding = function (batchIdx, cond) {
var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
};
var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
var landmarkTensors = output
.mul(tf.fill([batchSize, 136], inputSize))
.sub(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
})))
.div(tf.stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
})));
return landmarkTensors;
});
};
FaceLandmark68NetBase.prototype.forwardInput = function (input) {
var _this = this;
return tf.tidy(function () {
var out = _this.runNet(input);
return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
var height = _a[0], width = _a[1];
return ({ height: height, width: width });
}));
});
};
FaceLandmark68NetBase.prototype.forward = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _a;
return tslib_1.__generator(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
landmarkTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return tslib_1.__awaiter(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return tfjs_image_recognition_base_1.isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !tfjs_image_recognition_base_1.isEven(i); });
return [2 /*return*/, new FaceLandmarks68_1.FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new tfjs_image_recognition_base_1.Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? landmarksForBatch
: landmarksForBatch[0]];
}
});
});
};
return FaceLandmark68NetBase;
}(tfjs_image_recognition_base_1.NeuralNetwork));
exports.FaceLandmark68NetBase = FaceLandmark68NetBase;
//# sourceMappingURL=FaceLandmark68NetBase.js.map
\ No newline at end of file
build/faceLandmarkNet/FaceLandmark68NetBase.js.map 0 → 100644
{"version":3,"file":"FaceLandmark68NetBase.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/FaceLandmark68NetBase.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAwH;AAExH,8DAA6D;AAE7D;IAAsD,iDAAwB;IAK5E,+BAAY,KAAa;QAAzB,YACE,kBAAM,KAAK,CAAC,SAEb;QADC,KAAI,CAAC,MAAM,GAAG,KAAK,CAAA;;IACrB,CAAC;IAEM,sCAAM,GAAb,UAAc,CAAW;QACvB,MAAM,IAAI,KAAK,CAAI,IAAI,CAAC,MAAM,8BAA2B,CAAC,CAAA;IAC5D,CAAC;IAEM,2CAAW,GAAlB,UAAmB,MAAmB,EAAE,SAAiB,EAAE,kBAAgC;QAEzF,IAAM,eAAe,GAAG,kBAAkB,CAAC,GAAG,CAAC,UAAC,EAAiB;gBAAf,gBAAK,EAAE,kBAAM;YAC7D,IAAM,KAAK,GAAG,SAAS,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAA;YACjD,OAAO;gBACL,KAAK,EAAE,KAAK,GAAG,KAAK;gBACpB,MAAM,EAAE,MAAM,GAAG,KAAK;aACvB,CAAA;QACH,CAAC,CAAC,CAAA;QAEF,IAAM,SAAS,GAAG,eAAe,CAAC,MAAM,CAAA;QAExC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,uBAAuB,GAAG,UAAC,KAAa,EAAE,KAAa;gBAC3D,OAAA,EAAE,CAAC,KAAK,CAAC;oBACP,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;oBACpB,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,EAAE,KAAK,CAAC;iBACrB,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,IAAI,EAAE;YAHzB,CAGyB,CAAA;YAE3B,IAAM,UAAU,GAAG,UAAC,QAAgB,EAAE,IAAuC;gBACrE,IAAA,8BAA6C,EAA3C,gBAAK,EAAE,kBAAM,CAA8B;gBACnD,OAAO,IAAI,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAA;YAC/D,CAAC,CAAA;YACD,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAC/E,IAAM,WAAW,GAAG,UAAC,QAAgB,IAAK,OAAA,UAAU,CAAC,QAAQ,EAAE,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAArC,CAAqC,CAAA;YAE/E,IAAM,eAAe,GAAG,MAAM;iBAC3B,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,SAAS,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;iBACzC,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,WAAW,CAAC,QAAQ,CAAC,EACrB,WAAW,CAAC,QAAQ,CAAC,CACtB;YAHD,CAGC,CACF,CAAC,CAAC;iBACF,GAAG,CAAC,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,SAAS,CAAC,EAAE,UAAC,CAAC,EAAE,QAAQ;gBACrD,OAAA,uBAAuB,CACrB,eAAe,CAAC,QAAQ,CAAC,CAAC,KAAK,EAC/B,eAAe,CAAC,QAAQ,CAAC,CAAC,MAAM,CACjC;YAHD,CAGC,CACF,CAAC,CAAC,CAAA;YAEL,OAAO,eAA8B,CAAA;QACvC,CAAC,CAAC,CAAA;IACJ,CAAC;IAEM,4CAAY,GAAnB,UAAoB,KAAe;QAAnC,iBASC;QARC,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,GAAG,GAAG,KAAI,CAAC,MAAM,CAAC,KAAK,CAAC,CAAA;YAC9B,OAAO,KAAI,CAAC,WAAW,CACrB,GAAG,EACH,KAAK,CAAC,SAAmB,EACzB,KAAK,CAAC,eAAe,CAAC,GAAG,CAAC,UAAC,EAAe;oBAAd,cAAM,EAAE,aAAK;gBAAM,OAAA,CAAC,EAAE,MAAM,QAAA,EAAE,KAAK,OAAA,EAAE,CAAC;YAAnB,CAAmB,CAAC,CACpE,CAAA;QACH,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,uCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,+CAAe,GAA5B,UAA6B,KAAgB;;;;;;4BAC1B,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAClC,eAAe,GAAG,EAAE,CAAC,IAAI,CAC7B,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAEyB,qBAAM,OAAO,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAC7D,UAAO,cAAc,EAAE,QAAQ;;;;;4CACN,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;4CAAC,qBAAM,cAAc,CAAC,IAAI,EAAE,EAAA;;4CAAvD,cAAc,GAAG,cAAW,SAA2B,EAAC;4CACxD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,oCAAM,CAAC,CAAC,CAAC,EAAT,CAAS,CAAC,CAAA;4CACpD,OAAO,GAAG,cAAc,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,oCAAM,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC,CAAA;4CAE3D,sBAAO,IAAI,iCAAe,CACxB,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,IAAI,mCAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,EAAjC,CAAiC,CAAC,EAClE;oDACE,MAAM,EAAE,QAAQ,CAAC,cAAc,CAAC,QAAQ,CAAC;oDACzC,KAAK,EAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC;iDACzC,CACF,EAAA;;;iCACF,CACF,CAAC,EAAA;;wBAdI,iBAAiB,GAAG,SAcxB;wBAEF,eAAe,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAEzC,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,iBAAiB;gCACnB,CAAC,CAAC,iBAAiB,CAAC,CAAC,CAAC,EAAA;;;;KACzB;IACH,4BAAC;AAAD,CAAC,AAtGD,CAAsD,2CAAa,GAsGlE;AAtGY,sDAAqB"}
\ No newline at end of file
build/faceLandmarkNet/index.d.ts
import { FaceLandmarkNet } from './FaceLandmarkNet';
export * from './FaceLandmarkNet';
import { FaceLandmark68Net } from './FaceLandmark68Net';
export * from './FaceLandmark68Net';
export declare class FaceLandmarkNet extends FaceLandmark68Net {
}
export declare function createFaceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
export declare function faceLandmarkNet(weights: Float32Array): FaceLandmarkNet;
build/faceLandmarkNet/index.js
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var FaceLandmarkNet_1 = require("./FaceLandmarkNet");
tslib_1.__exportStar(require("./FaceLandmarkNet"), exports);
var FaceLandmark68Net_1 = require("./FaceLandmark68Net");
tslib_1.__exportStar(require("./FaceLandmark68Net"), exports);
var FaceLandmarkNet = /** @class */ (function (_super) {
tslib_1.__extends(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this;
}
return FaceLandmarkNet;
}(FaceLandmark68Net_1.FaceLandmark68Net));
exports.FaceLandmarkNet = FaceLandmarkNet;
function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet_1.FaceLandmarkNet();
var net = new FaceLandmarkNet();
net.extractWeights(weights);
return net;
}
......
build/faceLandmarkNet/index.js.map
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";;;AAAA,qDAAoD;AAEpD,4DAAkC;AAElC,+BAAsC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,iCAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,sDAIC;AAED,yBAAgC,OAAqB;IACnD,OAAO,CAAC,IAAI,CAAC,wGAAwG,CAAC,CAAA;IACtH,OAAO,qBAAqB,CAAC,OAAO,CAAC,CAAA;AACvC,CAAC;AAHD,0CAGC"}
\ No newline at end of file
{"version":3,"file":"index.js","sourceRoot":"","sources":["../../src/faceLandmarkNet/index.ts"],"names":[],"mappings":";;;AAAA,yDAAwD;AAExD,8DAAoC;AAEpC;IAAqC,2CAAiB;IAAtD;;IAAwD,CAAC;IAAD,sBAAC;AAAD,CAAC,AAAzD,CAAqC,qCAAiB,GAAG;AAA5C,0CAAe;AAE5B,+BAAsC,OAAqB;IACzD,IAAM,GAAG,GAAG,IAAI,eAAe,EAAE,CAAA;IACjC,GAAG,CAAC,cAAc,CAAC,OAAO,CAAC,CAAA;IAC3B,OAAO,GAAG,CAAA;AACZ,CAAC;AAJD,sDAIC;AAED,yBAAgC,OAAqB;IACnD,OAAO,CAAC,IAAI,CAAC,wGAAwG,CAAC,CAAA;IACtH,OAAO,qBAAqB,CAAC,OAAO,CAAC,CAAA;AACvC,CAAC;AAHD,0CAGC"}
\ No newline at end of file
build/faceRecognitionNet/FaceRecognitionNet.js
......@@ -18,7 +18,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
throw new Error('FaceRecognitionNet - load model before inference');
}
return tf.tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var batchTensor = input.toBatchTensor(150, true).toFloat();
var meanRgb = [122.782, 117.001, 104.298];
var normalized = tfjs_image_recognition_base_1.normalize(batchTensor, meanRgb).div(tf.scalar(256));
var out = convLayer_1.convDown(normalized, params.conv32_down);
......@@ -49,7 +49,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -61,7 +61,7 @@ var FaceRecognitionNet = /** @class */ (function (_super) {
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return tslib_1.__generator(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true)];
case 0: return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tf.tidy(function () { return tf.unstack(_this.forwardInput(netInput)); });
......
build/faceRecognitionNet/FaceRecognitionNet.js.map
{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAwG;AAExG,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,iDAAyD;AAIzD;IAAwC,8CAAwB;IAE9D;eACE,kBAAM,oBAAoB,CAAC;IAC7B,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAA;YAElD,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,uCAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;4BAAtD,sBAAO,SAAA,IAAI,GAAc,SAA6B,EAAC,EAAA;;;;KACxD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,CAAC,EAAA;;wBAAxC,QAAQ,GAAG,SAA6B;wBAExC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IAES,gDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,0CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,yBAAC;AAAD,CAAC,AA7ED,CAAwC,2CAAa,GA6EpD;AA7EY,gDAAkB"}
\ No newline at end of file
{"version":3,"file":"FaceRecognitionNet.js","sourceRoot":"","sources":["../../src/faceRecognitionNet/FaceRecognitionNet.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAwG;AAExG,yCAAuC;AACvC,iDAAgD;AAChD,6DAA4D;AAC5D,iDAAyD;AAIzD;IAAwC,8CAAwB;IAE9D;eACE,kBAAM,oBAAoB,CAAC;IAC7B,CAAC;IAEM,yCAAY,GAAnB,UAAoB,KAAe;QAEzB,IAAA,oBAAM,CAAS;QAEvB,IAAI,CAAC,MAAM,EAAE;YACX,MAAM,IAAI,KAAK,CAAC,kDAAkD,CAAC,CAAA;SACpE;QAED,OAAO,EAAE,CAAC,IAAI,CAAC;YACb,IAAM,WAAW,GAAG,KAAK,CAAC,aAAa,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC,OAAO,EAAE,CAAA;YAE5D,IAAM,OAAO,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAA;YAC3C,IAAM,UAAU,GAAG,uCAAS,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAgB,CAAA;YAErF,IAAI,GAAG,GAAG,oBAAQ,CAAC,UAAU,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAClD,GAAG,GAAG,EAAE,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAA;YAEpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,WAAW,CAAC,CAAA;YAC3C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YACpC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,QAAQ,CAAC,CAAA;YAEpC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YAErC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,YAAY,CAAC,CAAA;YAC5C,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,wBAAQ,CAAC,GAAG,EAAE,MAAM,CAAC,SAAS,CAAC,CAAA;YACrC,GAAG,GAAG,4BAAY,CAAC,GAAG,EAAE,MAAM,CAAC,gBAAgB,CAAC,CAAA;YAEhD,IAAM,SAAS,GAAG,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAgB,CAAA;YACjD,IAAM,cAAc,GAAG,EAAE,CAAC,MAAM,CAAC,SAAS,EAAE,MAAM,CAAC,EAAE,CAAC,CAAA;YAEtD,OAAO,cAAc,CAAA;QACvB,CAAC,CAAC,CAAA;IACJ,CAAC;IAEY,oCAAO,GAApB,UAAqB,KAAgB;;;;;;wBAC5B,KAAA,IAAI,CAAC,YAAY,CAAA;wBAAC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BAAhD,sBAAO,SAAA,IAAI,GAAc,SAAuB,EAAC,EAAA;;;;KAClD;IAEY,kDAAqB,GAAlC,UAAmC,KAAgB;;;;;;4BAChC,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;;wBAAlC,QAAQ,GAAG,SAAuB;wBAElC,qBAAqB,GAAG,EAAE,CAAC,IAAI,CACnC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,KAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,EAAvC,CAAuC,CAC9C,CAAA;wBAE+B,qBAAM,OAAO,CAAC,GAAG,CAAC,qBAAqB,CAAC,GAAG,CACzE,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,EAAE,EAAR,CAAQ,CACd,CAAC,EAAA;;wBAFI,uBAAuB,GAAG,SAEZ;wBAEpB,qBAAqB,CAAC,OAAO,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAA;wBAE/C,sBAAO,QAAQ,CAAC,YAAY;gCAC1B,CAAC,CAAC,uBAAuB;gCACzB,CAAC,CAAC,uBAAuB,CAAC,CAAC,CAAC,EAAA;;;;KAC/B;IAES,gDAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,0CAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,yBAAC;AAAD,CAAC,AA7ED,CAAwC,2CAAa,GA6EpD;AA7EY,gDAAkB"}
\ No newline at end of file
build/globalApi.d.ts
......@@ -5,17 +5,17 @@ import { FaceDetection } from './classes/FaceDetection';
import { FaceLandmarks68 } from './classes/FaceLandmarks68';
import { FullFaceDescription } from './classes/FullFaceDescription';
import { FaceDetectionNet } from './faceDetectionNet/FaceDetectionNet';
import { FaceLandmarkNet } from './faceLandmarkNet/FaceLandmarkNet';
import { FaceLandmark68Net } from './faceLandmarkNet/FaceLandmark68Net';
import { FaceRecognitionNet } from './faceRecognitionNet/FaceRecognitionNet';
import { Mtcnn } from './mtcnn/Mtcnn';
import { MtcnnForwardParams, MtcnnResult } from './mtcnn/types';
import { TinyYolov2 } from './tinyYolov2/TinyYolov2';
export declare const detectionNet: FaceDetectionNet;
export declare const landmarkNet: FaceLandmarkNet;
export declare const landmarkNet: FaceLandmark68Net;
export declare const recognitionNet: FaceRecognitionNet;
export declare const nets: {
ssdMobilenetv1: FaceDetectionNet;
faceLandmark68Net: FaceLandmarkNet;
faceLandmark68Net: FaceLandmark68Net;
faceRecognitionNet: FaceRecognitionNet;
mtcnn: Mtcnn;
tinyYolov2: TinyYolov2;
......
build/globalApi.js
......@@ -2,12 +2,12 @@
Object.defineProperty(exports, "__esModule", { value: true });
var allFacesFactory_1 = require("./allFacesFactory");
var FaceDetectionNet_1 = require("./faceDetectionNet/FaceDetectionNet");
var FaceLandmarkNet_1 = require("./faceLandmarkNet/FaceLandmarkNet");
var FaceLandmark68Net_1 = require("./faceLandmarkNet/FaceLandmark68Net");
var FaceRecognitionNet_1 = require("./faceRecognitionNet/FaceRecognitionNet");
var Mtcnn_1 = require("./mtcnn/Mtcnn");
var TinyYolov2_1 = require("./tinyYolov2/TinyYolov2");
exports.detectionNet = new FaceDetectionNet_1.FaceDetectionNet();
exports.landmarkNet = new FaceLandmarkNet_1.FaceLandmarkNet();
exports.landmarkNet = new FaceLandmark68Net_1.FaceLandmark68Net();
exports.recognitionNet = new FaceRecognitionNet_1.FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
......
build/globalApi.js.map
{"version":3,"file":"globalApi.js","sourceRoot":"","sources":["../src/globalApi.ts"],"names":[],"mappings":";;AAIA,qDAAmH;AAInH,wEAAuE;AACvE,qEAAoE;AACpE,8EAA6E;AAC7E,uCAAsC;AAEtC,sDAAqD;AAExC,QAAA,YAAY,GAAG,IAAI,mCAAgB,EAAE,CAAA;AACrC,QAAA,WAAW,GAAG,IAAI,iCAAe,EAAE,CAAA;AACnC,QAAA,cAAc,GAAG,IAAI,uCAAkB,EAAE,CAAA;AAEtD,8DAA8D;AAC9D,oDAAoD;AACvC,QAAA,IAAI,GAAG;IAClB,cAAc,EAAE,oBAAY;IAC5B,iBAAiB,EAAE,mBAAW;IAC9B,kBAAkB,EAAE,sBAAc;IAClC,KAAK,EAAE,IAAI,aAAK,EAAE;IAClB,UAAU,EAAE,IAAI,uBAAU,EAAE;CAC7B,CAAA;AAED,iCAAwC,GAAW;IACjD,OAAO,YAAI,CAAC,cAAc,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AACtC,CAAC;AAFD,0DAEC;AAED,+BAAsC,GAAW;IAC/C,OAAO,YAAI,CAAC,iBAAiB,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AACzC,CAAC;AAFD,sDAEC;AAED,kCAAyC,GAAW;IAClD,OAAO,YAAI,CAAC,kBAAkB,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC1C,CAAC;AAFD,4DAEC;AAED,wBAA+B,GAAW;IACxC,OAAO,YAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC7B,CAAC;AAFD,wCAEC;AAED,6BAAoC,GAAW;IAC7C,OAAO,YAAI,CAAC,UAAU,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAClC,CAAC;AAFD,kDAEC;AAED,gCAAuC,GAAW;IAChD,OAAO,uBAAuB,CAAC,GAAG,CAAC,CAAA;AACrC,CAAC;AAFD,wDAEC;AAED,oBAA2B,GAAW;IACpC,OAAO,OAAO,CAAC,GAAG,CAAC;QACjB,uBAAuB,CAAC,GAAG,CAAC;QAC5B,qBAAqB,CAAC,GAAG,CAAC;QAC1B,wBAAwB,CAAC,GAAG,CAAC;QAC7B,cAAc,CAAC,GAAG,CAAC;QACnB,mBAAmB,CAAC,GAAG,CAAC;KACzB,CAAC,CAAA;AACJ,CAAC;AARD,gCAQC;AAED,qBACE,KAAgB,EAChB,aAAsB,EACtB,UAAmB;IAEnB,OAAO,YAAI,CAAC,cAAc,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,EAAE,UAAU,CAAC,CAAA;AAC1E,CAAC;AAND,kCAMC;AAEY,QAAA,cAAc,GAAG,WAAW,CAAA;AAEzC,yBACE,KAAgB;IAEhB,OAAO,YAAI,CAAC,iBAAiB,CAAC,eAAe,CAAC,KAAK,CAAC,CAAA;AACtD,CAAC;AAJD,0CAIC;AAED,+BACE,KAAgB;IAEhB,OAAO,YAAI,CAAC,kBAAkB,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;AAC7D,CAAC;AAJD,sDAIC;AAED,eACE,KAAgB,EAChB,aAAiC;IAEjC,OAAO,YAAI,CAAC,KAAK,CAAC,OAAO,CAAC,KAAK,EAAE,aAAa,CAAC,CAAA;AACjD,CAAC;AALD,sBAKC;AAED,oBACE,KAAgB,EAChB,aAAsD;IAEtD,OAAO,YAAI,CAAC,UAAU,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,CAAA;AAC1D,CAAC;AALD,gCAKC;AAQY,QAAA,sBAAsB,GAAmC,+CAA6B,CACjG,YAAI,CAAC,cAAc,EACnB,YAAI,CAAC,iBAAiB,EACtB,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAQY,QAAA,kBAAkB,GAA+B,2CAAyB,CACrF,YAAI,CAAC,UAAU,EACf,YAAI,CAAC,iBAAiB,EACtB,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAQY,QAAA,aAAa,GAA0B,sCAAoB,CACtE,YAAI,CAAC,KAAK,EACV,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAEY,QAAA,QAAQ,GAAG,8BAAsB,CAAA"}
\ No newline at end of file
{"version":3,"file":"globalApi.js","sourceRoot":"","sources":["../src/globalApi.ts"],"names":[],"mappings":";;AAIA,qDAAmH;AAInH,wEAAuE;AACvE,yEAAwE;AACxE,8EAA6E;AAC7E,uCAAsC;AAEtC,sDAAqD;AAExC,QAAA,YAAY,GAAG,IAAI,mCAAgB,EAAE,CAAA;AACrC,QAAA,WAAW,GAAG,IAAI,qCAAiB,EAAE,CAAA;AACrC,QAAA,cAAc,GAAG,IAAI,uCAAkB,EAAE,CAAA;AAEtD,8DAA8D;AAC9D,oDAAoD;AACvC,QAAA,IAAI,GAAG;IAClB,cAAc,EAAE,oBAAY;IAC5B,iBAAiB,EAAE,mBAAW;IAC9B,kBAAkB,EAAE,sBAAc;IAClC,KAAK,EAAE,IAAI,aAAK,EAAE;IAClB,UAAU,EAAE,IAAI,uBAAU,EAAE;CAC7B,CAAA;AAED,iCAAwC,GAAW;IACjD,OAAO,YAAI,CAAC,cAAc,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AACtC,CAAC;AAFD,0DAEC;AAED,+BAAsC,GAAW;IAC/C,OAAO,YAAI,CAAC,iBAAiB,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AACzC,CAAC;AAFD,sDAEC;AAED,kCAAyC,GAAW;IAClD,OAAO,YAAI,CAAC,kBAAkB,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC1C,CAAC;AAFD,4DAEC;AAED,wBAA+B,GAAW;IACxC,OAAO,YAAI,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAC7B,CAAC;AAFD,wCAEC;AAED,6BAAoC,GAAW;IAC7C,OAAO,YAAI,CAAC,UAAU,CAAC,IAAI,CAAC,GAAG,CAAC,CAAA;AAClC,CAAC;AAFD,kDAEC;AAED,gCAAuC,GAAW;IAChD,OAAO,uBAAuB,CAAC,GAAG,CAAC,CAAA;AACrC,CAAC;AAFD,wDAEC;AAED,oBAA2B,GAAW;IACpC,OAAO,OAAO,CAAC,GAAG,CAAC;QACjB,uBAAuB,CAAC,GAAG,CAAC;QAC5B,qBAAqB,CAAC,GAAG,CAAC;QAC1B,wBAAwB,CAAC,GAAG,CAAC;QAC7B,cAAc,CAAC,GAAG,CAAC;QACnB,mBAAmB,CAAC,GAAG,CAAC;KACzB,CAAC,CAAA;AACJ,CAAC;AARD,gCAQC;AAED,qBACE,KAAgB,EAChB,aAAsB,EACtB,UAAmB;IAEnB,OAAO,YAAI,CAAC,cAAc,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,EAAE,UAAU,CAAC,CAAA;AAC1E,CAAC;AAND,kCAMC;AAEY,QAAA,cAAc,GAAG,WAAW,CAAA;AAEzC,yBACE,KAAgB;IAEhB,OAAO,YAAI,CAAC,iBAAiB,CAAC,eAAe,CAAC,KAAK,CAAC,CAAA;AACtD,CAAC;AAJD,0CAIC;AAED,+BACE,KAAgB;IAEhB,OAAO,YAAI,CAAC,kBAAkB,CAAC,qBAAqB,CAAC,KAAK,CAAC,CAAA;AAC7D,CAAC;AAJD,sDAIC;AAED,eACE,KAAgB,EAChB,aAAiC;IAEjC,OAAO,YAAI,CAAC,KAAK,CAAC,OAAO,CAAC,KAAK,EAAE,aAAa,CAAC,CAAA;AACjD,CAAC;AALD,sBAKC;AAED,oBACE,KAAgB,EAChB,aAAsD;IAEtD,OAAO,YAAI,CAAC,UAAU,CAAC,WAAW,CAAC,KAAK,EAAE,aAAa,CAAC,CAAA;AAC1D,CAAC;AALD,gCAKC;AAQY,QAAA,sBAAsB,GAAmC,+CAA6B,CACjG,YAAI,CAAC,cAAc,EACnB,YAAI,CAAC,iBAAiB,EACtB,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAQY,QAAA,kBAAkB,GAA+B,2CAAyB,CACrF,YAAI,CAAC,UAAU,EACf,YAAI,CAAC,iBAAiB,EACtB,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAQY,QAAA,aAAa,GAA0B,sCAAoB,CACtE,YAAI,CAAC,KAAK,EACV,YAAI,CAAC,kBAAkB,CACxB,CAAA;AAEY,QAAA,QAAQ,GAAG,8BAAsB,CAAA"}
\ No newline at end of file
build/mtcnn/Mtcnn.js
......@@ -23,7 +23,7 @@ var Mtcnn = /** @class */ (function (_super) {
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return tslib_1.__awaiter(this, void 0, void 0, function () {
var params, inputTensor, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
return tslib_1.__generator(this, function (_c) {
switch (_c.label) {
case 0:
......@@ -31,7 +31,6 @@ var Mtcnn = /** @class */ (function (_super) {
if (!params) {
throw new Error('Mtcnn - load model before inference');
}
inputTensor = input.inputs[0];
inputCanvas = input.canvases[0];
if (!inputCanvas) {
throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
......@@ -39,12 +38,11 @@ var Mtcnn = /** @class */ (function (_super) {
stats = {};
tsTotal = Date.now();
imgTensor = tf.tidy(function () {
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(inputTensor).toFloat());
return bgrToRgbTensor_1.bgrToRgbTensor(tf.expandDims(tf.fromPixels(inputCanvas)).toFloat());
});
onReturn = function (results) {
// dispose tensors on return
imgTensor.dispose();
input.dispose();
stats.total = Date.now() - tsTotal;
return results;
};
......@@ -103,7 +101,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results];
......@@ -119,7 +117,7 @@ var Mtcnn = /** @class */ (function (_super) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input, true, true)];
return [4 /*yield*/, tfjs_image_recognition_base_1.toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])];
}
......
build/mtcnn/Mtcnn.js.map
{"version":3,"file":"Mtcnn.js","sourceRoot":"","sources":["../../src/mtcnn/Mtcnn.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA0G;AAE1G,0DAAyD;AACzD,4DAA2D;AAC3D,mDAAkD;AAClD,mCAAqC;AACrC,iDAAgD;AAChD,+EAA8E;AAC9E,uDAAsD;AACtD,6DAA4D;AAC5D,6CAA4C;AAC5C,mCAAkC;AAClC,mCAAkC;AAClC,mCAAkC;AAGlC;IAA2B,iCAAwB;IAEjD;eACE,kBAAM,OAAO,CAAC;IAChB,CAAC;IAEY,4BAAY,GAAzB,UACE,KAAe,EACf,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAG9B,MAAM,GAAK,IAAI,OAAT,CAAS;wBAEvB,IAAI,CAAC,MAAM,EAAE;4BACX,MAAM,IAAI,KAAK,CAAC,qCAAqC,CAAC,CAAA;yBACvD;wBAEK,WAAW,GAAG,KAAK,CAAC,MAAM,CAAC,CAAC,CAAC,CAAA;wBAC7B,WAAW,GAAG,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;wBAErC,IAAI,CAAC,WAAW,EAAE;4BAChB,MAAM,IAAI,KAAK,CAAC,6GAA6G,CAAC,CAAA;yBAC/H;wBAEK,KAAK,GAAQ,EAAE,CAAA;wBAEf,OAAO,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBAEpB,SAAS,GAAG,EAAE,CAAC,IAAI,CAAC;4BACxB,OAAA,+BAAc,CACZ,EAAE,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,OAAO,EAAiB,CACpD;wBAFD,CAEC,CACF,CAAA;wBAEK,QAAQ,GAAG,UAAC,OAAY;4BAC5B,4BAA4B;4BAC5B,SAAS,CAAC,OAAO,EAAE,CAAA;4BACnB,KAAK,CAAC,OAAO,EAAE,CAAA;4BACf,KAAK,CAAC,KAAK,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,OAAO,CAAA;4BAClC,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAA;wBAEK,KAAkB,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,EAAzC,MAAM,QAAA,EAAE,KAAK,QAAA,CAA4B;wBAE1C,KAMF,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,2DAA4B,EAAE,EAAE,aAAa,CAAC,EALlE,WAAW,iBAAA,EACX,WAAW,iBAAA,EACX,YAAY,kBAAA,EACZ,eAAe,qBAAA,EACf,UAAU,gBAAA,CACwD;wBAE9D,MAAM,GAAG,CAAC,UAAU,IAAI,yBAAW,CAAC,WAAW,EAAE,WAAW,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC;6BAClF,MAAM,CAAC,UAAA,KAAK;4BACX,IAAM,KAAK,GAAG,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;4BACtD,OAAO,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,KAAK,EAAE,KAAK,CAAC,MAAM,CAAC,GAAG,kBAAS,CAAA;wBACxD,CAAC,CAAC;6BACD,KAAK,CAAC,CAAC,EAAE,YAAY,CAAC,CAAA;wBAEzB,KAAK,CAAC,MAAM,GAAG,MAAM,CAAA;wBACrB,KAAK,CAAC,OAAO,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,KAAK,IAAI,OAAA,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;wBAEzE,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACN,qBAAM,eAAM,CAAC,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAA9E,IAAI,GAAG,SAAuE;wBACpF,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAC9C,oFAAoF;wBACpF,6BAA6B;wBAC7B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAE9C,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE9B,OAAO,GAAG,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,GAAG,IAAK,OAAA,CAAC;4BAC5C,aAAa,EAAE,IAAI,6BAAa,CAC9B,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,EAChB,IAAI,kCAAI,CACN,GAAG,CAAC,IAAI,GAAG,KAAK,EAChB,GAAG,CAAC,GAAG,GAAG,MAAM,EAChB,GAAG,CAAC,KAAK,GAAG,KAAK,EACjB,GAAG,CAAC,MAAM,GAAG,MAAM,CACpB,EACD;gCACE,MAAM,QAAA;gCACN,KAAK,OAAA;6BACN,CACF;4BACD,aAAa,EAAE,IAAI,+BAAc,CAC/B,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,EAAhC,CAAgC,CAAC,EAC5D,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB;yBACF,CAAC,EAlB2C,CAkB3C,CAAC,CAAA;wBAEH,sBAAO,QAAQ,CAAC,EAAE,OAAO,SAAA,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;;;;KACpC;IAEY,uBAAO,GAApB,UACE,KAAgB,EAChB,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAG9B,KAAA,IAAI,CAAC,YAAY,CAAA;wBACrB,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,EAAE,IAAI,CAAC,EAAA;4BADrC,qBAAM,SAAA,IAAI,GACR,SAAmC;4BACnC,aAAa,EACd,EAAA;4BAJH,sBAAO,CACL,SAGC,CACF,CAAC,OAAO,EAAA;;;;KACV;IAEY,gCAAgB,GAA7B,UACE,KAAgB,EAChB,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAE/B,KAAA,IAAI,CAAC,YAAY,CAAA;wBACtB,qBAAM,wCAAU,CAAC,KAAK,EAAE,IAAI,EAAE,IAAI,CAAC,EAAA;4BADrC,sBAAO,SAAA,IAAI,GACT,SAAmC;4BACnC,aAAa,EACd,EAAA;;;;KACF;IAED,8CAA8C;IACpC,mCAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,6BAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,YAAC;AAAD,CAAC,AA5ID,CAA2B,2CAAa,GA4IvC;AA5IY,sBAAK"}
\ No newline at end of file
{"version":3,"file":"Mtcnn.js","sourceRoot":"","sources":["../../src/mtcnn/Mtcnn.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA0G;AAE1G,0DAAyD;AACzD,4DAA2D;AAC3D,mDAAkD;AAClD,mCAAqC;AACrC,iDAAgD;AAChD,+EAA8E;AAC9E,uDAAsD;AACtD,6DAA4D;AAC5D,6CAA4C;AAC5C,mCAAkC;AAClC,mCAAkC;AAClC,mCAAkC;AAGlC;IAA2B,iCAAwB;IAEjD;eACE,kBAAM,OAAO,CAAC;IAChB,CAAC;IAEY,4BAAY,GAAzB,UACE,KAAe,EACf,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAG9B,MAAM,GAAK,IAAI,OAAT,CAAS;wBAEvB,IAAI,CAAC,MAAM,EAAE;4BACX,MAAM,IAAI,KAAK,CAAC,qCAAqC,CAAC,CAAA;yBACvD;wBAEK,WAAW,GAAG,KAAK,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAA;wBAErC,IAAI,CAAC,WAAW,EAAE;4BAChB,MAAM,IAAI,KAAK,CAAC,6GAA6G,CAAC,CAAA;yBAC/H;wBAEK,KAAK,GAAQ,EAAE,CAAA;wBAEf,OAAO,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBAEpB,SAAS,GAAG,EAAE,CAAC,IAAI,CAAC;4BACxB,OAAA,+BAAc,CACZ,EAAE,CAAC,UAAU,CAAC,EAAE,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC,CAAC,OAAO,EAAiB,CACnE;wBAFD,CAEC,CACF,CAAA;wBAEK,QAAQ,GAAG,UAAC,OAAY;4BAC5B,4BAA4B;4BAC5B,SAAS,CAAC,OAAO,EAAE,CAAA;4BACnB,KAAK,CAAC,KAAK,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,OAAO,CAAA;4BAClC,OAAO,OAAO,CAAA;wBAChB,CAAC,CAAA;wBAEK,KAAkB,SAAS,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,EAAzC,MAAM,QAAA,EAAE,KAAK,QAAA,CAA4B;wBAE1C,KAMF,MAAM,CAAC,MAAM,CAAC,EAAE,EAAE,2DAA4B,EAAE,EAAE,aAAa,CAAC,EALlE,WAAW,iBAAA,EACX,WAAW,iBAAA,EACX,YAAY,kBAAA,EACZ,eAAe,qBAAA,EACf,UAAU,gBAAA,CACwD;wBAE9D,MAAM,GAAG,CAAC,UAAU,IAAI,yBAAW,CAAC,WAAW,EAAE,WAAW,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC;6BAClF,MAAM,CAAC,UAAA,KAAK;4BACX,IAAM,KAAK,GAAG,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;4BACtD,OAAO,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,KAAK,EAAE,KAAK,CAAC,MAAM,CAAC,GAAG,kBAAS,CAAA;wBACxD,CAAC,CAAC;6BACD,KAAK,CAAC,CAAC,EAAE,YAAY,CAAC,CAAA;wBAEzB,KAAK,CAAC,MAAM,GAAG,MAAM,CAAA;wBACrB,KAAK,CAAC,OAAO,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,KAAK,IAAI,OAAA,mCAAgB,CAAC,KAAK,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;wBAEzE,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACN,qBAAM,eAAM,CAAC,SAAS,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAA9E,IAAI,GAAG,SAAuE;wBACpF,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAC9C,oFAAoF;wBACpF,6BAA6B;wBAC7B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAEpC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,MAAM,EAAE;4BACtB,sBAAO,QAAQ,CAAC,EAAE,OAAO,EAAE,EAAE,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;yBACxC;wBAED,KAAK,CAAC,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,MAAM,CAAA;wBAE9C,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACF,qBAAM,eAAM,CAAC,WAAW,EAAE,IAAI,CAAC,KAAK,EAAE,eAAe,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,IAAI,EAAE,KAAK,CAAC,EAAA;;wBAApF,IAAI,GAAG,SAA6E;wBAC1F,KAAK,CAAC,YAAY,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE9B,OAAO,GAAG,IAAI,CAAC,KAAK,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,GAAG,IAAK,OAAA,CAAC;4BAC5C,aAAa,EAAE,IAAI,6BAAa,CAC9B,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,EAChB,IAAI,kCAAI,CACN,GAAG,CAAC,IAAI,GAAG,KAAK,EAChB,GAAG,CAAC,GAAG,GAAG,MAAM,EAChB,GAAG,CAAC,KAAK,GAAG,KAAK,EACjB,GAAG,CAAC,MAAM,GAAG,MAAM,CACpB,EACD;gCACE,MAAM,QAAA;gCACN,KAAK,OAAA;6BACN,CACF;4BACD,aAAa,EAAE,IAAI,+BAAc,CAC/B,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,UAAA,EAAE,IAAI,OAAA,EAAE,CAAC,GAAG,CAAC,IAAI,mCAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,EAAhC,CAAgC,CAAC,EAC5D,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAClB;yBACF,CAAC,EAlB2C,CAkB3C,CAAC,CAAA;wBAEH,sBAAO,QAAQ,CAAC,EAAE,OAAO,SAAA,EAAE,KAAK,OAAA,EAAE,CAAC,EAAA;;;;KACpC;IAEY,uBAAO,GAApB,UACE,KAAgB,EAChB,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAG9B,KAAA,IAAI,CAAC,YAAY,CAAA;wBACrB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BADzB,qBAAM,SAAA,IAAI,GACR,SAAuB;4BACvB,aAAa,EACd,EAAA;4BAJH,sBAAO,CACL,SAGC,CACF,CAAC,OAAO,EAAA;;;;KACV;IAEY,gCAAgB,GAA7B,UACE,KAAgB,EAChB,aAAsC;QAAtC,8BAAA,EAAA,kBAAsC;;;;;;wBAE/B,KAAA,IAAI,CAAC,YAAY,CAAA;wBACtB,qBAAM,wCAAU,CAAC,KAAK,CAAC,EAAA;4BADzB,sBAAO,SAAA,IAAI,GACT,SAAuB;4BACvB,aAAa,EACd,EAAA;;;;KACF;IAED,8CAA8C;IACpC,mCAAmB,GAA7B,UAA8B,GAAuB;QACnD,OAAO,yCAAmB,CAAC,GAAG,CAAC,CAAA;IACjC,CAAC;IAES,6BAAa,GAAvB,UAAwB,OAAqB;QAC3C,OAAO,6BAAa,CAAC,OAAO,CAAC,CAAA;IAC/B,CAAC;IACH,YAAC;AAAD,CAAC,AA1ID,CAA2B,2CAAa,GA0IvC;AA1IY,sBAAK"}
\ No newline at end of file
build/mtcnn/MtcnnBox.d.ts 0 → 100644
import { Box } from 'tfjs-image-recognition-base';
export declare class MtcnnBox extends Box<MtcnnBox> {
constructor(left: number, top: number, right: number, bottom: number);
}
build/mtcnn/MtcnnBox.js 0 → 100644
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var MtcnnBox = /** @class */ (function (_super) {
tslib_1.__extends(MtcnnBox, _super);
function MtcnnBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
}
return MtcnnBox;
}(tfjs_image_recognition_base_1.Box));
exports.MtcnnBox = MtcnnBox;
//# sourceMappingURL=MtcnnBox.js.map
\ No newline at end of file
build/mtcnn/MtcnnBox.js.map 0 → 100644
{"version":3,"file":"MtcnnBox.js","sourceRoot":"","sources":["../../src/mtcnn/MtcnnBox.ts"],"names":[],"mappings":";;;AAAA,2EAAkD;AAElD;IAA8B,oCAAa;IACzC,kBAAY,IAAY,EAAE,GAAW,EAAE,KAAa,EAAE,MAAc;eAClE,kBAAM,EAAE,IAAI,MAAA,EAAE,GAAG,KAAA,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,EAAE,IAAI,CAAC;IAC3C,CAAC;IACH,eAAC;AAAD,CAAC,AAJD,CAA8B,iCAAG,GAIhC;AAJY,4BAAQ"}
\ No newline at end of file
build/mtcnn/extractImagePatches.d.ts
import * as tf from '@tensorflow/tfjs-core';
import { BoundingBox, Dimensions } from 'tfjs-image-recognition-base';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: BoundingBox[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
import { Box, Dimensions } from 'tfjs-image-recognition-base';
export declare function extractImagePatches(img: HTMLCanvasElement, boxes: Box[], {width, height}: Dimensions): Promise<tf.Tensor4D[]>;
build/mtcnn/extractImagePatches.js
......@@ -2,8 +2,8 @@
Object.defineProperty(exports, "__esModule", { value: true });
var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var normalize_1 = require("./normalize");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var normalize_1 = require("./normalize");
function extractImagePatches(img, boxes, _a) {
var width = _a.width, height = _a.height;
return tslib_1.__awaiter(this, void 0, void 0, function () {
......
build/mtcnn/extractImagePatches.js.map
{"version":3,"file":"extractImagePatches.js","sourceRoot":"","sources":["../../src/mtcnn/extractImagePatches.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAE5C,yCAAwC;AACxC,2EAAyG;AAEzG,6BACE,GAAsB,EACtB,KAAoB,EACpB,EAA6B;QAA3B,gBAAK,EAAE,kBAAM;;;;;;;oBAIT,MAAM,GAAG,iDAAmB,CAAC,GAAG,CAAC,CAAA;oBAEvB,qBAAM,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,UAAM,GAAG;;;gCAE7C,KAAmB,GAAG,CAAC,YAAY,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,KAAK,CAAC,EAAxD,CAAC,OAAA,EAAE,EAAE,QAAA,EAAE,CAAC,OAAA,EAAE,EAAE,QAAA,CAA4C;gCAE1D,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,OAAO,GAAG,MAAM,CAAC,YAAY,CAAC,KAAK,EAAE,KAAK,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,CAAC,CAAA;gCAE7E,sBAAO,iBAAiB,CAAC,OAAO,CAAC,EAAA;;6BAClC,CAAC,CAAC,EAAA;;oBATG,OAAO,GAAG,SASb;oBAEG,iBAAiB,GAAe,EAAE,CAAA;oBAExC,OAAO,CAAC,OAAO,CAAC,UAAA,GAAG;wBACjB,IAAM,KAAK,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;wBAC7C,IAAM,QAAQ,GAAG,iDAAmB,CAAC,KAAK,CAAC,CAAA;wBAC3C,QAAQ,CAAC,SAAS,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;wBACpC,IAAA,sDAAI,CAA+C;wBAE3D,IAAM,QAAQ,GAAG,EAAE,CAAA;wBACnB,cAAc;wBACd,KAAI,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,IAAE,CAAC,EAAE;4BACpC,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;yBACvB;wBACD,iBAAiB,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAA;oBAClC,CAAC,CAAC,CAAA;oBAGF,sBAAO,iBAAiB,CAAC,GAAG,CAAC,UAAA,IAAI;4BAC/B,IAAM,CAAC,GAAG,EAAE,CAAC,IAAI,CAAC;gCAChB,IAAM,gBAAgB,GAAG,EAAE,CAAC,SAAS,CACnC,EAAE,CAAC,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAC,CAAC,EACxC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAC,OAAO,EAAiB,CAAA;gCAE1B,OAAO,qBAAS,CAAC,gBAAgB,CAAC,CAAA;4BACpC,CAAC,CAAC,CAAA;4BACF,OAAO,CAAC,CAAA;wBACV,CAAC,CAAC,EAAA;;;;CACH;AAlDD,kDAkDC"}
\ No newline at end of file
{"version":3,"file":"extractImagePatches.js","sourceRoot":"","sources":["../../src/mtcnn/extractImagePatches.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAiG;AAEjG,yCAAwC;AAExC,6BACE,GAAsB,EACtB,KAAY,EACZ,EAA6B;QAA3B,gBAAK,EAAE,kBAAM;;;;;;;oBAIT,MAAM,GAAG,iDAAmB,CAAC,GAAG,CAAC,CAAA;oBAEvB,qBAAM,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,UAAM,GAAG;;;gCAE7C,KAAmB,GAAG,CAAC,YAAY,CAAC,GAAG,CAAC,MAAM,EAAE,GAAG,CAAC,KAAK,CAAC,EAAxD,CAAC,OAAA,EAAE,EAAE,QAAA,EAAE,CAAC,OAAA,EAAE,EAAE,QAAA,CAA4C;gCAE1D,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,KAAK,GAAG,CAAC,GAAG,CAAC,CAAA;gCACb,OAAO,GAAG,MAAM,CAAC,YAAY,CAAC,KAAK,EAAE,KAAK,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,EAAE,CAAC,EAAE,GAAG,KAAK,CAAC,CAAC,CAAA;gCAE7E,sBAAO,iBAAiB,CAAC,OAAO,CAAC,EAAA;;6BAClC,CAAC,CAAC,EAAA;;oBATG,OAAO,GAAG,SASb;oBAEG,iBAAiB,GAAe,EAAE,CAAA;oBAExC,OAAO,CAAC,OAAO,CAAC,UAAA,GAAG;wBACjB,IAAM,KAAK,GAAG,0CAAY,CAAC,EAAE,KAAK,OAAA,EAAE,MAAM,QAAA,EAAE,CAAC,CAAA;wBAC7C,IAAM,QAAQ,GAAG,iDAAmB,CAAC,KAAK,CAAC,CAAA;wBAC3C,QAAQ,CAAC,SAAS,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,KAAK,EAAE,MAAM,CAAC,CAAA;wBACpC,IAAA,sDAAI,CAA+C;wBAE3D,IAAM,QAAQ,GAAG,EAAE,CAAA;wBACnB,cAAc;wBACd,KAAI,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,IAAE,CAAC,EAAE;4BACpC,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAA;4BAC1B,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAA;yBACvB;wBACD,iBAAiB,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAA;oBAClC,CAAC,CAAC,CAAA;oBAGF,sBAAO,iBAAiB,CAAC,GAAG,CAAC,UAAA,IAAI;4BAC/B,IAAM,CAAC,GAAG,EAAE,CAAC,IAAI,CAAC;gCAChB,IAAM,gBAAgB,GAAG,EAAE,CAAC,SAAS,CACnC,EAAE,CAAC,QAAQ,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,KAAK,EAAE,MAAM,EAAE,CAAC,CAAC,CAAC,EACxC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CACb,CAAC,OAAO,EAAiB,CAAA;gCAE1B,OAAO,qBAAS,CAAC,gBAAgB,CAAC,CAAA;4BACpC,CAAC,CAAC,CAAA;4BACF,OAAO,CAAC,CAAA;wBACV,CAAC,CAAC,EAAA;;;;CACH;AAlDD,kDAkDC"}
\ No newline at end of file
build/mtcnn/stage1.js
......@@ -4,6 +4,7 @@ var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var config_1 = require("./config");
var getSizesForScale_1 = require("./getSizesForScale");
var MtcnnBox_1 = require("./MtcnnBox");
var normalize_1 = require("./normalize");
var PNet_1 = require("./PNet");
function rescaleAndNormalize(x, scale) {
......@@ -27,7 +28,7 @@ function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold
var boundingBoxes = indices.map(function (idx) {
var cell = new tfjs_image_recognition_base_1.BoundingBox(Math.round((idx.y * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.x * config_1.CELL_STRIDE + 1) / scale), Math.round((idx.y * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale), Math.round((idx.x * config_1.CELL_STRIDE + config_1.CELL_SIZE) / scale));
var score = scoresTensor.get(idx.y, idx.x);
var region = new tfjs_image_recognition_base_1.BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
var region = new MtcnnBox_1.MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
return {
cell: cell,
score: score,
......
build/mtcnn/stage1.js.map
{"version":3,"file":"stage1.js","sourceRoot":"","sources":["../../src/mtcnn/stage1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,2EAAoF;AAEpF,mCAAkD;AAClD,uDAAsD;AACtD,yCAAwC;AACxC,+BAA8B;AAG9B,6BAA6B,CAAc,EAAE,KAAa;IACxD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEP,IAAA,iEAA6D,EAA3D,kBAAM,EAAE,gBAAK,CAA8C;QACnE,IAAM,OAAO,GAAG,EAAE,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;QAC3D,IAAM,UAAU,GAAG,qBAAS,CAAC,OAAO,CAAC,CAAA;QAErC,OAAQ,EAAE,CAAC,SAAS,CAAC,UAAU,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAiB,CAAA;IAChE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,8BACE,YAAyB,EACzB,aAA0B,EAC1B,KAAa,EACb,cAAsB;IAGtB,sDAAsD;IACtD,IAAM,OAAO,GAAY,EAAE,CAAA;IAC3B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;QAC9C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;YAC9C,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,IAAI,cAAc,EAAE;gBAC5C,OAAO,CAAC,IAAI,CAAC,IAAI,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;aAC9B;SACF;KACF;IAED,IAAM,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG;QACnC,IAAM,IAAI,GAAG,IAAI,yCAAW,CAC1B,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,CAAC,CAAC,GAAG,KAAK,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,CAAC,CAAC,GAAG,KAAK,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,kBAAS,CAAC,GAAG,KAAK,CAAC,EACrD,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,kBAAS,CAAC,GAAG,KAAK,CAAC,CACtD,CAAA;QAED,IAAM,KAAK,GAAG,YAAY,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAA;QAE5C,IAAM,MAAM,GAAG,IAAI,yCAAW,CAC5B,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CACnC,CAAA;QAED,OAAO;YACL,IAAI,MAAA;YACJ,KAAK,OAAA;YACL,MAAM,QAAA;SACP,CAAA;IACH,CAAC,CAAC,CAAA;IAEF,OAAO,aAAa,CAAA;AACtB,CAAC;AAED,gBACE,SAAsB,EACtB,MAAgB,EAChB,cAAsB,EACtB,MAAkB,EAClB,KAAU;IAEV,KAAK,CAAC,MAAM,GAAG,EAAE,CAAA;IAEjB,IAAM,WAAW,GAAG,MAAM,CAAC,GAAG,CAAC,UAAC,KAAK,IAAK,OAAA,EAAE,CAAC,IAAI,CAAC;QAChD,IAAM,aAAa,GAAQ,EAAE,KAAK,OAAA,EAAE,CAAA;QACpC,IAAM,OAAO,GAAG,mBAAmB,CAAC,SAAS,EAAE,KAAK,CAAC,CAAA;QAErD,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACb,IAAA,iCAAyC,EAAvC,cAAI,EAAE,oBAAO,CAA0B;QAC/C,aAAa,CAAC,IAAI,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QAEpC,IAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACzE,IAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAgB,CAAA;QAE3D,OAAO;YACL,YAAY,cAAA;YACZ,aAAa,eAAA;YACb,KAAK,OAAA;YACL,aAAa,eAAA;SACd,CAAA;IACH,CAAC,CAAC,EAjBwC,CAiBxC,CAAC,CAAA;IAEH,IAAM,aAAa,GAAG,WAAW,CAAC,GAAG,CAAC,UAAC,EAAqD;YAAnD,8BAAY,EAAE,gCAAa,EAAE,gBAAK,EAAE,gCAAa;QACxF,IAAM,aAAa,GAAG,oBAAoB,CACxC,YAAY,EACZ,aAAa,EACb,KAAK,EACL,cAAc,CACf,CAAA;QAED,YAAY,CAAC,OAAO,EAAE,CAAA;QACtB,aAAa,CAAC,OAAO,EAAE,CAAA;QAEvB,IAAI,CAAC,aAAa,CAAC,MAAM,EAAE;YACzB,KAAK,CAAC,MAAM,CAAC,IAAI,CAAC,aAAa,CAAC,CAAA;YAChC,OAAO,EAAE,CAAA;SACV;QAED,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACnB,IAAM,OAAO,GAAG,+CAAiB,CAC/B,aAAa,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,IAAI,EAAT,CAAS,CAAC,EACpC,aAAa,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,KAAK,EAAV,CAAU,CAAC,EACrC,GAAG,CACJ,CAAA;QACD,aAAa,CAAC,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QACnC,aAAa,CAAC,QAAQ,GAAG,OAAO,CAAC,MAAM,CAAA;QAEvC,KAAK,CAAC,MAAM,CAAC,IAAI,CAAC,aAAa,CAAC,CAAA;QAChC,OAAO,OAAO,CAAC,GAAG,CAAC,UAAA,MAAM,IAAI,OAAA,aAAa,CAAC,MAAM,CAAC,EAArB,CAAqB,CAAC,CAAA;IACrD,CAAC,CAAC,CAAA;IAEF,IAAM,QAAQ,GAAG,aAAa,CAAC,MAAM,CACnC,UAAC,GAAG,EAAE,KAAK,IAAK,OAAA,GAAG,CAAC,MAAM,CAAC,KAAK,CAAC,EAAjB,CAAiB,EAAE,EAAE,CACtC,CAAA;IAED,IAAI,UAAU,GAAkB,EAAE,CAAA;IAClC,IAAI,WAAW,GAAa,EAAE,CAAA;IAE9B,IAAI,QAAQ,CAAC,MAAM,GAAG,CAAC,EAAE;QACvB,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACnB,IAAM,OAAO,GAAG,+CAAiB,CAC/B,QAAQ,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,IAAI,EAAT,CAAS,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,KAAK,EAAV,CAAU,CAAC,EAChC,GAAG,CACJ,CAAA;QACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QAElC,WAAW,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,QAAQ,CAAC,GAAG,CAAC,CAAC,KAAK,EAAnB,CAAmB,CAAC,CAAA;QACrD,UAAU,GAAG,OAAO;aACjB,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,QAAQ,CAAC,GAAG,CAAC,EAAb,CAAa,CAAC;aACzB,GAAG,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAClB,OAAA,IAAI,yCAAW,CACb,IAAI,CAAC,IAAI,GAAG,CAAC,MAAM,CAAC,IAAI,GAAG,IAAI,CAAC,KAAK,CAAC,EACtC,IAAI,CAAC,GAAG,GAAG,CAAC,MAAM,CAAC,GAAG,GAAG,IAAI,CAAC,MAAM,CAAC,EACrC,IAAI,CAAC,KAAK,GAAG,CAAC,MAAM,CAAC,KAAK,GAAG,IAAI,CAAC,KAAK,CAAC,EACxC,IAAI,CAAC,MAAM,GAAG,CAAC,MAAM,CAAC,MAAM,GAAG,IAAI,CAAC,MAAM,CAAC,CAC5C,CAAC,QAAQ,EAAE,CAAC,KAAK,EAAE;QALpB,CAKoB,CACrB,CAAA;KAEJ;IAED,OAAO;QACL,KAAK,EAAE,UAAU;QACjB,MAAM,EAAE,WAAW;KACpB,CAAA;AAEH,CAAC;AA5FD,wBA4FC"}
\ No newline at end of file
{"version":3,"file":"stage1.js","sourceRoot":"","sources":["../../src/mtcnn/stage1.ts"],"names":[],"mappings":";;AAAA,0CAA4C;AAC5C,2EAAoF;AAEpF,mCAAkD;AAClD,uDAAsD;AACtD,uCAAsC;AACtC,yCAAwC;AACxC,+BAA8B;AAG9B,6BAA6B,CAAc,EAAE,KAAa;IACxD,OAAO,EAAE,CAAC,IAAI,CAAC;QAEP,IAAA,iEAA6D,EAA3D,kBAAM,EAAE,gBAAK,CAA8C;QACnE,IAAM,OAAO,GAAG,EAAE,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC,EAAE,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAA;QAC3D,IAAM,UAAU,GAAG,qBAAS,CAAC,OAAO,CAAC,CAAA;QAErC,OAAQ,EAAE,CAAC,SAAS,CAAC,UAAU,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAiB,CAAA;IAChE,CAAC,CAAC,CAAA;AACJ,CAAC;AAED,8BACE,YAAyB,EACzB,aAA0B,EAC1B,KAAa,EACb,cAAsB;IAGtB,sDAAsD;IACtD,IAAM,OAAO,GAAY,EAAE,CAAA;IAC3B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;QAC9C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;YAC9C,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,IAAI,cAAc,EAAE;gBAC5C,OAAO,CAAC,IAAI,CAAC,IAAI,mCAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAA;aAC9B;SACF;KACF;IAED,IAAM,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG;QACnC,IAAM,IAAI,GAAG,IAAI,yCAAW,CAC1B,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,CAAC,CAAC,GAAG,KAAK,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,CAAC,CAAC,GAAG,KAAK,CAAC,EAC7C,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,kBAAS,CAAC,GAAG,KAAK,CAAC,EACrD,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,oBAAW,GAAG,kBAAS,CAAC,GAAG,KAAK,CAAC,CACtD,CAAA;QAED,IAAM,KAAK,GAAG,YAAY,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAA;QAE5C,IAAM,MAAM,GAAG,IAAI,mBAAQ,CACzB,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAClC,aAAa,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CACnC,CAAA;QAED,OAAO;YACL,IAAI,MAAA;YACJ,KAAK,OAAA;YACL,MAAM,QAAA;SACP,CAAA;IACH,CAAC,CAAC,CAAA;IAEF,OAAO,aAAa,CAAA;AACtB,CAAC;AAED,gBACE,SAAsB,EACtB,MAAgB,EAChB,cAAsB,EACtB,MAAkB,EAClB,KAAU;IAEV,KAAK,CAAC,MAAM,GAAG,EAAE,CAAA;IAEjB,IAAM,WAAW,GAAG,MAAM,CAAC,GAAG,CAAC,UAAC,KAAK,IAAK,OAAA,EAAE,CAAC,IAAI,CAAC;QAChD,IAAM,aAAa,GAAQ,EAAE,KAAK,OAAA,EAAE,CAAA;QACpC,IAAM,OAAO,GAAG,mBAAmB,CAAC,SAAS,EAAE,KAAK,CAAC,CAAA;QAErD,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACb,IAAA,iCAAyC,EAAvC,cAAI,EAAE,oBAAO,CAA0B;QAC/C,aAAa,CAAC,IAAI,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QAEpC,IAAM,YAAY,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAgB,CAAA;QACzE,IAAM,aAAa,GAAG,EAAE,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC,CAAC,CAAgB,CAAA;QAE3D,OAAO;YACL,YAAY,cAAA;YACZ,aAAa,eAAA;YACb,KAAK,OAAA;YACL,aAAa,eAAA;SACd,CAAA;IACH,CAAC,CAAC,EAjBwC,CAiBxC,CAAC,CAAA;IAEH,IAAM,aAAa,GAAG,WAAW,CAAC,GAAG,CAAC,UAAC,EAAqD;YAAnD,8BAAY,EAAE,gCAAa,EAAE,gBAAK,EAAE,gCAAa;QACxF,IAAM,aAAa,GAAG,oBAAoB,CACxC,YAAY,EACZ,aAAa,EACb,KAAK,EACL,cAAc,CACf,CAAA;QAED,YAAY,CAAC,OAAO,EAAE,CAAA;QACtB,aAAa,CAAC,OAAO,EAAE,CAAA;QAEvB,IAAI,CAAC,aAAa,CAAC,MAAM,EAAE;YACzB,KAAK,CAAC,MAAM,CAAC,IAAI,CAAC,aAAa,CAAC,CAAA;YAChC,OAAO,EAAE,CAAA;SACV;QAED,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACnB,IAAM,OAAO,GAAG,+CAAiB,CAC/B,aAAa,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,IAAI,EAAT,CAAS,CAAC,EACpC,aAAa,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,KAAK,EAAV,CAAU,CAAC,EACrC,GAAG,CACJ,CAAA;QACD,aAAa,CAAC,GAAG,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QACnC,aAAa,CAAC,QAAQ,GAAG,OAAO,CAAC,MAAM,CAAA;QAEvC,KAAK,CAAC,MAAM,CAAC,IAAI,CAAC,aAAa,CAAC,CAAA;QAChC,OAAO,OAAO,CAAC,GAAG,CAAC,UAAA,MAAM,IAAI,OAAA,aAAa,CAAC,MAAM,CAAC,EAArB,CAAqB,CAAC,CAAA;IACrD,CAAC,CAAC,CAAA;IAEF,IAAM,QAAQ,GAAG,aAAa,CAAC,MAAM,CACnC,UAAC,GAAG,EAAE,KAAK,IAAK,OAAA,GAAG,CAAC,MAAM,CAAC,KAAK,CAAC,EAAjB,CAAiB,EAAE,EAAE,CACtC,CAAA;IAED,IAAI,UAAU,GAAkB,EAAE,CAAA;IAClC,IAAI,WAAW,GAAa,EAAE,CAAA;IAE9B,IAAI,QAAQ,CAAC,MAAM,GAAG,CAAC,EAAE;QACvB,IAAI,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;QACnB,IAAM,OAAO,GAAG,+CAAiB,CAC/B,QAAQ,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,IAAI,EAAT,CAAS,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,UAAA,IAAI,IAAI,OAAA,IAAI,CAAC,KAAK,EAAV,CAAU,CAAC,EAChC,GAAG,CACJ,CAAA;QACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;QAElC,WAAW,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,QAAQ,CAAC,GAAG,CAAC,CAAC,KAAK,EAAnB,CAAmB,CAAC,CAAA;QACrD,UAAU,GAAG,OAAO;aACjB,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,QAAQ,CAAC,GAAG,CAAC,EAAb,CAAa,CAAC;aACzB,GAAG,CAAC,UAAC,EAAgB;gBAAd,cAAI,EAAE,kBAAM;YAClB,OAAA,IAAI,yCAAW,CACb,IAAI,CAAC,IAAI,GAAG,CAAC,MAAM,CAAC,IAAI,GAAG,IAAI,CAAC,KAAK,CAAC,EACtC,IAAI,CAAC,GAAG,GAAG,CAAC,MAAM,CAAC,GAAG,GAAG,IAAI,CAAC,MAAM,CAAC,EACrC,IAAI,CAAC,KAAK,GAAG,CAAC,MAAM,CAAC,KAAK,GAAG,IAAI,CAAC,KAAK,CAAC,EACxC,IAAI,CAAC,MAAM,GAAG,CAAC,MAAM,CAAC,MAAM,GAAG,IAAI,CAAC,MAAM,CAAC,CAC5C,CAAC,QAAQ,EAAE,CAAC,KAAK,EAAE;QALpB,CAKoB,CACrB,CAAA;KAEJ;IAED,OAAO;QACL,KAAK,EAAE,UAAU;QACjB,MAAM,EAAE,WAAW;KACpB,CAAA;AAEH,CAAC;AA5FD,wBA4FC"}
\ No newline at end of file
build/mtcnn/stage2.d.ts
import { BoundingBox } from 'tfjs-image-recognition-base';
import { Box } from 'tfjs-image-recognition-base';
import { RNetParams } from './types';
export declare function stage2(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
boxes: BoundingBox[];
export declare function stage2(img: HTMLCanvasElement, inputBoxes: Box[], scoreThreshold: number, params: RNetParams, stats: any): Promise<{
boxes: Box<any>[];
scores: number[];
}>;
build/mtcnn/stage2.js
......@@ -4,6 +4,7 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches");
var MtcnnBox_1 = require("./MtcnnBox");
var RNet_1 = require("./RNet");
function stage2(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -47,7 +48,7 @@ function stage2(img, inputBoxes, scoreThreshold, params, stats) {
indicesNms = tfjs_image_recognition_base_1.nonMaxSuppression(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) {
return new tfjs_image_recognition_base_1.BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
return new MtcnnBox_1.MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
});
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......
build/mtcnn/stage2.js.map
{"version":3,"file":"stage2.js","sourceRoot":"","sources":["../../src/mtcnn/stage2.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAA6E;AAE7E,6DAA4D;AAC5D,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAyB,EACzB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,UAAU,CAAC,GAAG,CAAC,EAAf,CAAe,CAAC,CAAA;oBACnD,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAkB,EAAE,CAAA;oBAC9B,WAAW,GAAa,EAAE,CAAA;oBAE9B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAC5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,CACJ,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE5B,YAAU,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG;4BAChC,OAAA,IAAI,yCAAW,CACb,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CACzC;wBALD,CAKC,CACF,CAAA;wBAED,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,aAAa,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,SAAO,CAAC,CAAC,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;qBAClF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;yBACpB,EAAA;;;;CACF;AAtED,wBAsEC"}
\ No newline at end of file
{"version":3,"file":"stage2.js","sourceRoot":"","sources":["../../src/mtcnn/stage2.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAqE;AAErE,6DAA4D;AAC5D,uCAAsC;AACtC,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAiB,EACjB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,aAAa,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,UAAU,CAAC,GAAG,CAAC,EAAf,CAAe,CAAC,CAAA;oBACnD,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAU,EAAE,CAAA;oBACtB,WAAW,GAAa,EAAE,CAAA;oBAE9B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAC5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,CACJ,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAE5B,YAAU,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG;4BAChC,OAAA,IAAI,mBAAQ,CACV,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EACxC,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CACzC;wBALD,CAKC,CACF,CAAA;wBAED,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,aAAa,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,SAAO,CAAC,CAAC,CAAC,CAAC,EAAxC,CAAwC,CAAC,CAAA;qBAClF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;yBACpB,EAAA;;;;CACF;AAtED,wBAsEC"}
\ No newline at end of file
build/mtcnn/stage3.d.ts
import { BoundingBox, Point } from 'tfjs-image-recognition-base';
import { BoundingBox, Box, Point } from 'tfjs-image-recognition-base';
import { ONetParams } from './types';
export declare function stage3(img: HTMLCanvasElement, inputBoxes: BoundingBox[], scoreThreshold: number, params: ONetParams, stats: any): Promise<{
boxes: BoundingBox[];
boxes: Box<any>[];
scores: number[];
points: Point[][];
}>;
build/mtcnn/stage3.js
......@@ -4,6 +4,7 @@ var tslib_1 = require("tslib");
var tf = require("@tensorflow/tfjs-core");
var tfjs_image_recognition_base_1 = require("tfjs-image-recognition-base");
var extractImagePatches_1 = require("./extractImagePatches");
var MtcnnBox_1 = require("./MtcnnBox");
var ONet_1 = require("./ONet");
function stage3(img, inputBoxes, scoreThreshold, params, stats) {
return tslib_1.__awaiter(this, void 0, void 0, function () {
......@@ -38,7 +39,7 @@ function stage3(img, inputBoxes, scoreThreshold, params, stats) {
var idx = _a.idx;
return idx;
});
filteredRegions = indices.map(function (idx) { return new tfjs_image_recognition_base_1.BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredRegions = indices.map(function (idx) { return new MtcnnBox_1.MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; });
......
build/mtcnn/stage3.js.map
{"version":3,"file":"stage3.js","sourceRoot":"","sources":["../../src/mtcnn/stage3.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAoF;AAEpF,6DAA4D;AAC5D,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAyB,EACzB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,eAAe,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,IAAI,yCAAW,CACxD,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAChC,EAL0C,CAK1C,CAAC,CAAA;oBACI,aAAa,GAAG,OAAO;yBAC1B,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,UAAU,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAA7C,CAA6C,CAAC,CAAA;oBAC3D,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAkB,EAAE,CAAA;oBAC9B,WAAW,GAAa,EAAE,CAAA;oBAC1B,MAAM,GAAc,EAAE,CAAA;oBAE1B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAE5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,EACH,KAAK,CACN,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAElC,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,aAAa,CAAC,GAAG,CAAC,EAAlB,CAAkB,CAAC,CAAA;wBACtD,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,MAAM,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC;4BAC7B,OAAA,KAAK,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,KAAK;gCAC5B,OAAA,IAAI,mCAAK,CACP,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EACvF,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,GAAG,CAAC,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAC5F;4BAHD,CAGC,CACF;wBALD,CAKC,CACF,CAAA;qBACF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;wBAClB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;4BACnB,MAAM,QAAA;yBACP,EAAA;;;;CAEF;AAlFD,wBAkFC"}
\ No newline at end of file
{"version":3,"file":"stage3.js","sourceRoot":"","sources":["../../src/mtcnn/stage3.ts"],"names":[],"mappings":";;;AAAA,0CAA4C;AAC5C,2EAAyF;AAEzF,6DAA4D;AAC5D,uCAAsC;AACtC,+BAA8B;AAG9B,gBACE,GAAsB,EACtB,UAAyB,EACzB,cAAsB,EACtB,MAAkB,EAClB,KAAU;;;;;;oBAGN,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACA,qBAAM,yCAAmB,CAAC,GAAG,EAAE,UAAU,EAAE,EAAE,KAAK,EAAE,EAAE,EAAE,MAAM,EAAE,EAAE,EAAE,CAAC,EAAA;;oBAAlF,UAAU,GAAG,SAAqE;oBACxF,KAAK,CAAC,0BAA0B,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAElD,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;oBACT,QAAQ,GAAG,UAAU,CAAC,GAAG,CAC7B,UAAA,SAAS;wBACP,IAAM,GAAG,GAAG,WAAI,CAAC,SAAS,EAAE,MAAM,CAAC,CAAA;wBACnC,SAAS,CAAC,OAAO,EAAE,CAAA;wBACnB,OAAO,GAAG,CAAA;oBACZ,CAAC,CACF,CAAA;oBACD,KAAK,CAAC,WAAW,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;oBAE7B,YAAY,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC;wBACtC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,GAAG,CAAC,MAAM,EAAV,CAAU,CAAC,CAAC;wBAC5C,CAAC,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,MAAM,CAAA;oBACP,KAAA,CAAA,KAAA,KAAK,CAAA,CAAC,IAAI,CAAA;oBAAC,qBAAM,YAAY,CAAC,IAAI,EAAE,EAAA;;oBAA7C,MAAM,GAAG,cAAW,SAAyB,EAAC;oBACpD,YAAY,CAAC,OAAO,EAAE,CAAA;oBAEhB,OAAO,GAAG,MAAM;yBACnB,GAAG,CAAC,UAAC,KAAK,EAAE,GAAG,IAAK,OAAA,CAAC,EAAE,KAAK,OAAA,EAAE,GAAG,KAAA,EAAE,CAAC,EAAhB,CAAgB,CAAC;yBACrC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;yBACrC,GAAG,CAAC,UAAC,EAAO;4BAAL,YAAG;wBAAO,OAAA,GAAG;oBAAH,CAAG,CAAC,CAAA;oBAElB,eAAe,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,IAAI,mBAAQ,CACrD,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAC/B,QAAQ,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAChC,EAL0C,CAK1C,CAAC,CAAA;oBACI,aAAa,GAAG,OAAO;yBAC1B,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC,IAAK,OAAA,UAAU,CAAC,GAAG,CAAC,CAAC,SAAS,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAA7C,CAA6C,CAAC,CAAA;oBAC3D,cAAc,GAAG,OAAO,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAA;oBAElD,UAAU,GAAU,EAAE,CAAA;oBACtB,WAAW,GAAa,EAAE,CAAA;oBAC1B,MAAM,GAAc,EAAE,CAAA;oBAE1B,IAAI,aAAa,CAAC,MAAM,GAAG,CAAC,EAAE;wBAE5B,EAAE,GAAG,IAAI,CAAC,GAAG,EAAE,CAAA;wBACT,UAAU,GAAG,+CAAiB,CAClC,aAAa,EACb,cAAc,EACd,GAAG,EACH,KAAK,CACN,CAAA;wBACD,KAAK,CAAC,UAAU,GAAG,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,CAAA;wBAElC,UAAU,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,aAAa,CAAC,GAAG,CAAC,EAAlB,CAAkB,CAAC,CAAA;wBACtD,WAAW,GAAG,UAAU,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,cAAc,CAAC,GAAG,CAAC,EAAnB,CAAmB,CAAC,CAAA;wBACxD,MAAM,GAAG,UAAU,CAAC,GAAG,CAAC,UAAC,GAAG,EAAE,CAAC;4BAC7B,OAAA,KAAK,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,KAAK;gCAC5B,OAAA,IAAI,mCAAK,CACP,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,EACvF,CAAC,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,EAAE,KAAK,GAAG,CAAC,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAC5F;4BAHD,CAGC,CACF;wBALD,CAKC,CACF,CAAA;qBACF;oBAED,QAAQ,CAAC,OAAO,CAAC,UAAA,CAAC;wBAChB,CAAC,CAAC,OAAO,CAAC,OAAO,EAAE,CAAA;wBACnB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;wBAClB,CAAC,CAAC,MAAM,CAAC,OAAO,EAAE,CAAA;oBACpB,CAAC,CAAC,CAAA;oBAEF,sBAAO;4BACL,KAAK,EAAE,UAAU;4BACjB,MAAM,EAAE,WAAW;4BACnB,MAAM,QAAA;yBACP,EAAA;;;;CAEF;AAlFD,wBAkFC"}
\ No newline at end of file
dist/face-api.js
......@@ -307,137 +307,164 @@
return pts.reduce(function (sum$$1, pt) { return sum$$1.add(pt); }, new Point(0, 0))
.div(new Point(pts.length, pts.length));
}
var Rect = /** @class */ (function () {
function Rect(x, y, width, height) {
this.x = x;
this.y = y;
this.width = width;
this.height = height;
function range$1(num, start, step$$1) {
return Array(num).fill(0).map(function (_, i) { return start + (i * step$$1); });
}
Object.defineProperty(Rect.prototype, "right", {
function isValidNumber(num) {
return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
}
function isValidProbablitiy(num) {
return isValidNumber(num) && 0 <= num && num <= 1.0;
}
var Box = /** @class */ (function () {
// TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
// allowNegativeDimensions flag again
function Box(_box, allowNegativeDimensions) {
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
var box = _box || {};
var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
if (!isRect && !isBbox) {
throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
}
var _a = isRect
? [box.x, box.y, box.width, box.height]
: [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
this._x = x;
this._y = y;
this._width = width;
this._height = height;
}
Box.isRect = function (rect) {
return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
};
Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
if (!Box.isRect(box)) {
throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
}
if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
}
};
Object.defineProperty(Box.prototype, "x", {
get: function () {
return this.x + this.width;
return this._x;
},
enumerable: true,
configurable: true
});
Object.defineProperty(Rect.prototype, "bottom", {
Object.defineProperty(Box.prototype, "y", {
get: function () {
return this.y + this.height;
return this._y;
},
enumerable: true,
configurable: true
});
Rect.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new Rect(x, y, width, height);
};
Rect.prototype.pad = function (padX, padY) {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return new Rect(x - (padX / 2), y - (padY / 2), width + padX, height + padY);
};
Rect.prototype.floor = function () {
return new Rect(Math.floor(this.x), Math.floor(this.y), Math.floor(this.width), Math.floor(this.height));
};
Rect.prototype.toBoundingBox = function () {
return new BoundingBox(this.x, this.y, this.x + this.width, this.y + this.height);
};
Rect.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
var clippedY = Math.max(y, 0);
var newWidth = right - clippedX;
var newHeight = bottom - clippedY;
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
return (new Rect(clippedX, clippedY, clippedWidth, clippedHeight)).floor();
};
return Rect;
}());
var BoundingBox = /** @class */ (function () {
function BoundingBox(_left, _top, _right, _bottom) {
this._left = _left;
this._top = _top;
this._right = _right;
this._bottom = _bottom;
}
Object.defineProperty(BoundingBox.prototype, "left", {
Object.defineProperty(Box.prototype, "width", {
get: function () {
return this._left;
return this._width;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "top", {
Object.defineProperty(Box.prototype, "height", {
get: function () {
return this._top;
return this._height;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "right", {
Object.defineProperty(Box.prototype, "left", {
get: function () {
return this._right;
return this.x;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "bottom", {
Object.defineProperty(Box.prototype, "top", {
get: function () {
return this._bottom;
return this.y;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "width", {
Object.defineProperty(Box.prototype, "right", {
get: function () {
return this.right - this.left;
return this.x + this.width;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "height", {
Object.defineProperty(Box.prototype, "bottom", {
get: function () {
return this.bottom - this.top;
return this.y + this.height;
},
enumerable: true,
configurable: true
});
Object.defineProperty(BoundingBox.prototype, "area", {
Object.defineProperty(Box.prototype, "area", {
get: function () {
return this.width * this.height;
},
enumerable: true,
configurable: true
});
BoundingBox.prototype.toSquare = function () {
var _a = this, left = _a.left, top = _a.top, right = _a.right, bottom = _a.bottom;
var off = (Math.abs(this.width - this.height) / 2);
if (this.width < this.height) {
left -= off;
right += off;
Box.prototype.round = function () {
var _a = [this.x, this.y, this.width, this.height]
.map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
return new Box({ x: x, y: y, width: width, height: height });
};
Box.prototype.floor = function () {
var _a = [this.x, this.y, this.width, this.height]
.map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
return new Box({ x: x, y: y, width: width, height: height });
};
Box.prototype.toSquare = function () {
var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var diff = Math.abs(width - height);
if (width < height) {
x -= (diff / 2);
width += diff;
}
if (this.height < this.width) {
top -= off;
bottom += off;
if (height < width) {
y -= (diff / 2);
height += diff;
}
return new BoundingBox(left, top, right, bottom);
return new Box({ x: x, y: y, width: width, height: height });
};
BoundingBox.prototype.round = function () {
return new BoundingBox(Math.round(this.left), Math.round(this.top), Math.round(this.right), Math.round(this.bottom));
Box.prototype.rescale = function (s) {
var scaleX = isDimensions(s) ? s.width : s;
var scaleY = isDimensions(s) ? s.height : s;
return new Box({
x: this.x * scaleX,
y: this.y * scaleY,
width: this.width * scaleX,
height: this.height * scaleY
});
};
BoundingBox.prototype.padAtBorders = function (imageHeight, imageWidth) {
Box.prototype.pad = function (padX, padY) {
var _a = [
this.x - (padX / 2),
this.y - (padY / 2),
this.width + padX,
this.height + padY
], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
return new Box({ x: x, y: y, width: width, height: height });
};
Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
var clippedX = Math.max(x, 0);
var clippedY = Math.max(y, 0);
var newWidth = right - clippedX;
var newHeight = bottom - clippedY;
var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
};
Box.prototype.padAtBorders = function (imageHeight, imageWidth) {
var w = this.width + 1;
var h = this.height + 1;
var dx = 1;
......@@ -466,19 +493,132 @@
}
return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
};
BoundingBox.prototype.calibrate = function (region) {
return new BoundingBox(this.left + (region.left * this.width), this.top + (region.top * this.height), this.right + (region.right * this.width), this.bottom + (region.bottom * this.height)).toSquare().round();
Box.prototype.calibrate = function (region) {
return new Box({
left: this.left + (region.left * this.width),
top: this.top + (region.top * this.height),
right: this.right + (region.right * this.width),
bottom: this.bottom + (region.bottom * this.height)
}).toSquare().round();
};
BoundingBox.prototype.rescale = function (s) {
var scaleX = isDimensions(s) ? s.width : s;
var scaleY = isDimensions(s) ? s.height : s;
return new BoundingBox(this.left * scaleX, this.top * scaleY, this.right * scaleX, this.bottom * scaleY);
return Box;
}());
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
/* global Reflect, Promise */
var extendStatics$1 = function(d, b) {
extendStatics$1 = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
return extendStatics$1(d, b);
};
function __extends$1(d, b) {
extendStatics$1(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
}
var __assign$1 = function() {
__assign$1 = Object.assign || function __assign(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
}
return t;
};
BoundingBox.prototype.toRect = function () {
return new Rect(this.left, this.top, this.width, this.height);
return __assign$1.apply(this, arguments);
};
function __awaiter$1(thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator$1(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
var BoundingBox = /** @class */ (function (_super) {
__extends$1(BoundingBox, _super);
function BoundingBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;
}
return BoundingBox;
}());
}(Box));
var LabeledBox = /** @class */ (function (_super) {
__extends$1(LabeledBox, _super);
function LabeledBox(box, label) {
var _this = _super.call(this, box) || this;
_this._label = label;
return _this;
}
LabeledBox.assertIsValidLabeledBox = function (box, callee) {
Box.assertIsValidBox(box, callee);
if (!isValidNumber(box.label)) {
throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
}
};
Object.defineProperty(LabeledBox.prototype, "label", {
get: function () {
return this._label;
},
enumerable: true,
configurable: true
});
return LabeledBox;
}(Box));
var Rect = /** @class */ (function (_super) {
__extends$1(Rect, _super);
function Rect(x, y, width, height) {
return _super.call(this, { x: x, y: y, width: width, height: height }) || this;
}
return Rect;
}(Box));
var ObjectDetection = /** @class */ (function () {
function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
......@@ -560,6 +700,38 @@
return ObjectDetection;
}());
var PredictedBox = /** @class */ (function (_super) {
__extends$1(PredictedBox, _super);
function PredictedBox(box, label, score, classScore) {
var _this = _super.call(this, box, label) || this;
_this._score = score;
_this._classScore = classScore;
return _this;
}
PredictedBox.assertIsValidPredictedBox = function (box, callee) {
LabeledBox.assertIsValidLabeledBox(box, callee);
if (!isValidProbablitiy(box.score)
|| !isValidProbablitiy(box.classScore)) {
throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
}
};
Object.defineProperty(PredictedBox.prototype, "score", {
get: function () {
return this._score;
},
enumerable: true,
configurable: true
});
Object.defineProperty(PredictedBox.prototype, "classScore", {
get: function () {
return this._classScore;
},
enumerable: true,
configurable: true
});
return PredictedBox;
}(LabeledBox));
function disposeUnusedWeightTensors(weightMap, paramMappings) {
Object.keys(weightMap).forEach(function (path) {
if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
......@@ -761,83 +933,6 @@
});
}
/*! *****************************************************************************
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use
this file except in compliance with the License. You may obtain a copy of the
License at http://www.apache.org/licenses/LICENSE-2.0
THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
MERCHANTABLITY OR NON-INFRINGEMENT.
See the Apache Version 2.0 License for specific language governing permissions
and limitations under the License.
***************************************************************************** */
/* global Reflect, Promise */
var extendStatics$1 = function(d, b) {
extendStatics$1 = Object.setPrototypeOf ||
({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
return extendStatics$1(d, b);
};
function __extends$1(d, b) {
extendStatics$1(d, b);
function __() { this.constructor = d; }
d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
}
var __assign$1 = function() {
__assign$1 = Object.assign || function __assign(t) {
for (var s, i = 1, n = arguments.length; i < n; i++) {
s = arguments[i];
for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
}
return t;
};
return __assign$1.apply(this, arguments);
};
function __awaiter$1(thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
}
function __generator$1(thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
}
function imageTensorToCanvas(imgTensor, canvas) {
return __awaiter$1(this, void 0, void 0, function () {
var targetCanvas, _a, height, width, numChannels, imgTensor3D;
......@@ -857,7 +952,8 @@
});
}
function imageToSquare(input, inputSize) {
function imageToSquare(input, inputSize, centerImage) {
if (centerImage === void 0) { centerImage = false; }
if (!(input instanceof HTMLImageElement || input instanceof HTMLCanvasElement)) {
throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
}
......@@ -867,7 +963,10 @@
var height = scale * dims.height;
var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
var inputCanvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, 0, 0, width, height);
var offset = Math.abs(width - height) / 2;
var dx = centerImage && width < height ? offset : 0;
var dy = centerImage && height < width ? offset : 0;
getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
return targetCanvas;
}
......@@ -898,7 +997,8 @@
* Pads the smaller dimension of an image tensor with zeros, such that width === height.
*
* @param imgTensor The image tensor.
* @param isCenterImage (optional, default: false) If true, add padding on both sides of the image, such that the image.
* @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
* both sides of the minor dimension oof the image.
* @returns The padded tensor with width === height.
*/
function padToSquare(imgTensor, isCenterImage) {
......@@ -932,46 +1032,41 @@
}
var NetInput = /** @class */ (function () {
function NetInput(inputs, isBatchInput, keepCanvases) {
if (isBatchInput === void 0) { isBatchInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
function NetInput(inputs, treatAsBatchInput) {
if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
var _this = this;
this._inputs = [];
this._imageTensors = [];
this._canvases = [];
this._isManaged = false;
this._isBatchInput = false;
this._treatAsBatchInput = false;
this._inputDimensions = [];
this._paddings = [];
if (isTensor4D(inputs)) {
this._inputs = unstack(inputs);
if (!Array.isArray(inputs)) {
throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
}
if (Array.isArray(inputs)) {
this._inputs = inputs.map(function (input, idx) {
this._treatAsBatchInput = treatAsBatchInput;
this._batchSize = inputs.length;
inputs.forEach(function (input, idx) {
if (isTensor3D(input)) {
// TODO: make sure not to dispose original tensors passed in by the user
return clone(input);
_this._imageTensors[idx] = input;
_this._inputDimensions[idx] = input.shape;
return;
}
if (isTensor4D(input)) {
var shape = input.shape;
var batchSize = shape[0];
var batchSize = input.shape[0];
if (batchSize !== 1) {
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
return input.reshape(shape.slice(1));
_this._imageTensors[idx] = input;
_this._inputDimensions[idx] = input.shape.slice(1);
return;
}
var canvas = input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input);
if (keepCanvases) {
_this._canvases[idx] = canvas;
}
return fromPixels(canvas);
_this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
});
}
this._isBatchInput = this.batchSize > 1 || isBatchInput;
this._inputDimensions = this._inputs.map(function (t) { return t.shape; });
}
Object.defineProperty(NetInput.prototype, "inputs", {
Object.defineProperty(NetInput.prototype, "imageTensors", {
get: function () {
return this._inputs;
return this._imageTensors;
},
enumerable: true,
configurable: true
......@@ -983,23 +1078,16 @@
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isManaged", {
get: function () {
return this._isManaged;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "isBatchInput", {
get: function () {
return this._isBatchInput;
return this.batchSize > 1 || this._treatAsBatchInput;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "batchSize", {
get: function () {
return this._inputs.length;
return this._batchSize;
},
enumerable: true,
configurable: true
......@@ -1011,13 +1099,6 @@
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "paddings", {
get: function () {
return this._paddings;
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "inputSize", {
get: function () {
return this._inputSize;
......@@ -1025,22 +1106,17 @@
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "relativePaddings", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getRelativePaddings(batchIdx); });
},
enumerable: true,
configurable: true
});
Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
get: function () {
var _this = this;
return Array(this.inputs.length).fill(0).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
},
enumerable: true,
configurable: true
});
NetInput.prototype.getInput = function (batchIdx) {
return this.canvases[batchIdx] || this.imageTensors[batchIdx];
};
NetInput.prototype.getInputDimensions = function (batchIdx) {
return this._inputDimensions[batchIdx];
};
......@@ -1050,12 +1126,6 @@
NetInput.prototype.getInputWidth = function (batchIdx) {
return this._inputDimensions[batchIdx][1];
};
NetInput.prototype.getPaddings = function (batchIdx) {
return this._paddings[batchIdx];
};
NetInput.prototype.getRelativePaddings = function (batchIdx) {
return new Point((this.getPaddings(batchIdx).x + this.getInputWidth(batchIdx)) / this.getInputWidth(batchIdx), (this.getPaddings(batchIdx).y + this.getInputHeight(batchIdx)) / this.getInputHeight(batchIdx));
};
NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
if (typeof this.inputSize !== 'number') {
throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
......@@ -1064,40 +1134,39 @@
var height = this.getInputHeight(batchIdx);
return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
};
/**
* Create a batch tensor from all input canvases and tensors
* with size [batchSize, inputSize, inputSize, 3].
*
* @param inputSize Height and width of the tensor.
* @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
* both sides of the minor dimension oof the image.
* @returns The batch tensor.
*/
NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
var _this = this;
if (isCenterInputs === void 0) { isCenterInputs = true; }
this._inputSize = inputSize;
return tidy(function () {
var inputTensors = _this._inputs.map(function (inputTensor) {
var _a = inputTensor.shape, originalHeight = _a[0], originalWidth = _a[1];
var imgTensor = inputTensor.expandDims().toFloat();
var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
var input = _this.getInput(batchIdx);
if (input instanceof Tensor) {
var imgTensor = isTensor4D(input) ? input : input.expandDims();
imgTensor = padToSquare(imgTensor, isCenterInputs);
var _b = imgTensor.shape.slice(1), heightAfterPadding = _b[0], widthAfterPadding = _b[1];
if (heightAfterPadding !== inputSize || widthAfterPadding !== inputSize) {
if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
}
_this._paddings.push(new Point(widthAfterPadding - originalWidth, heightAfterPadding - originalHeight));
return imgTensor;
});
var batchTensor = stack(inputTensors).as4D(_this.batchSize, inputSize, inputSize, 3);
if (_this.isManaged) {
_this.dispose();
return imgTensor.as3D(inputSize, inputSize, 3);
}
if (input instanceof HTMLCanvasElement) {
return fromPixels(imageToSquare(input, inputSize, isCenterInputs));
}
throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
});
var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
return batchTensor;
});
};
/**
* By setting the isManaged flag, all newly created tensors will be
* automatically disposed after the batch tensor has been created
*/
NetInput.prototype.managed = function () {
this._isManaged = true;
return this;
};
NetInput.prototype.dispose = function () {
this._inputs.forEach(function (t) { return t.dispose(); });
};
return NetInput;
}());
......@@ -1106,27 +1175,17 @@
* to be finished loading.
*
* @param input The input, which can be a media element or an array of different media elements.
* @param manageCreatedInput If a new NetInput instance is created from the inputs, this flag
* determines, whether to set the NetInput as managed or not.
* @returns A NetInput instance, which can be passed into one of the neural networks.
*/
function toNetInput(inputs, manageCreatedInput, keepCanvases) {
if (manageCreatedInput === void 0) { manageCreatedInput = false; }
if (keepCanvases === void 0) { keepCanvases = false; }
function toNetInput(inputs) {
return __awaiter$1(this, void 0, void 0, function () {
var afterCreate, inputArgArray, getIdxHint, inputArray;
var inputArgArray, getIdxHint, inputArray;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0:
if (inputs instanceof NetInput) {
return [2 /*return*/, inputs];
}
afterCreate = function (netInput) { return manageCreatedInput
? netInput.managed()
: netInput; };
if (isTensor4D(inputs)) {
return [2 /*return*/, afterCreate(new NetInput(inputs))];
}
inputArgArray = Array.isArray(inputs)
? inputs
: [inputs];
......@@ -1155,7 +1214,7 @@
case 1:
// wait for all media elements being loaded
_a.sent();
return [2 /*return*/, afterCreate(new NetInput(inputArray, Array.isArray(inputs), keepCanvases))];
return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
}
});
});
......@@ -1584,29 +1643,30 @@
*/
function extractFaces(input, detections) {
return __awaiter$1(this, void 0, void 0, function () {
var canvas, netInput, ctx, boxes;
return __generator$1(this, function (_a) {
switch (_a.label) {
var canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
canvas = input;
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 3];
return [4 /*yield*/, toNetInput(input, true)];
if (!!(input instanceof HTMLCanvasElement)) return [3 /*break*/, 5];
return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _a.sent();
netInput = _b.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
throw new Error('extractFaces - batchSize > 1 not supported');
}
return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])];
case 2:
canvas = _a.sent();
if (netInput.isManaged) {
netInput.dispose();
}
_a.label = 3;
tensorOrCanvas = netInput.getInput(0);
if (!(tensorOrCanvas instanceof HTMLCanvasElement)) return [3 /*break*/, 2];
_a = tensorOrCanvas;
return [3 /*break*/, 4];
case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];
case 3:
_a = _b.sent();
_b.label = 4;
case 4:
canvas = _a;
_b.label = 5;
case 5:
ctx = getContext2dOrThrow(canvas);
boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(canvas.width, canvas.height).getBox().floor()
......@@ -1630,41 +1690,28 @@
* Using this method is faster then extracting a canvas for each face and
* converting them to tensors individually.
*
* @param input The image that face detection has been performed on.
* @param imageTensor The image tensor that face detection has been performed on.
* @param detections The face detection results or face bounding boxes for that image.
* @returns Tensors of the corresponding image region for each detected face.
*/
function extractFaceTensors(input, detections) {
function extractFaceTensors(imageTensor, detections) {
return __awaiter$1(this, void 0, void 0, function () {
var netInput;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
if (netInput.batchSize > 1) {
if (netInput.isManaged) {
netInput.dispose();
}
if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
throw new Error('extractFaceTensors - batchSize > 1 not supported');
}
return [2 /*return*/, tidy(function () {
var imgTensor = netInput.inputs[0].expandDims().toFloat();
var _a = imgTensor.shape.slice(1), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
var boxes = detections.map(function (det) { return det instanceof FaceDetection
? det.forSize(imgWidth, imgHeight).getBox()
: det; })
.map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
var faceTensors = boxes.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
return slice4d(imgTensor, [0, y, x, 0], [1, height, width, numChannels]);
return slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
});
if (netInput.isManaged) {
netInput.dispose();
}
return faceTensors;
})];
}
});
});
}
......@@ -2147,6 +2194,24 @@
SizeType["LG"] = "lg";
})(SizeType || (SizeType = {}));
function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
return function (channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
};
}
function loadSeparableConvParamsFactory(extractWeightEntry) {
return function (prefix) {
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
};
}
var isNumber = function (arg) { return typeof arg === 'number'; };
function validateConfig(config) {
if (!config) {
......@@ -2183,6 +2248,9 @@
var INPUT_SIZES = { xs: 224, sm: 320, md: 416, lg: 608 };
var CELL_SIZE = 32;
var DEFAULT_FILTER_SIZES = [
3, 16, 32, 64, 128, 256, 512, 1024, 1024
];
function leaky(x) {
return tidy(function () {
......@@ -2222,33 +2290,28 @@
var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
return { conv: conv, bn: bn };
}
function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
var bias = tensor1d(extractWeights(channelsOut));
paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
extractSeparableConvParams: extractSeparableConvParams
};
}
function extractParams$1(weights, withSeparableConvs, boxEncodingSize) {
function extractParams$1(weights, withSeparableConvs, boxEncodingSize, filterSizes) {
var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
var paramMappings = [];
var _b = extractorsFactory$2(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
var extractConvFn = withSeparableConvs ? extractSeparableConvParams : extractConvWithBatchNormParams;
var conv0 = extractConvFn(3, 16, 'conv0');
var conv1 = extractConvFn(16, 32, 'conv1');
var conv2 = extractConvFn(32, 64, 'conv2');
var conv3 = extractConvFn(64, 128, 'conv3');
var conv4 = extractConvFn(128, 256, 'conv4');
var conv5 = extractConvFn(256, 512, 'conv5');
var conv6 = extractConvFn(512, 1024, 'conv6');
var conv7 = extractConvFn(1024, 1024, 'conv7');
var conv8 = extractConvParams(1024, 5 * boxEncodingSize, 1, 'conv8');
var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
var conv0 = extractConvFn(s0, s1, 'conv0');
var conv1 = extractConvFn(s1, s2, 'conv1');
var conv2 = extractConvFn(s2, s3, 'conv2');
var conv3 = extractConvFn(s3, s4, 'conv3');
var conv4 = extractConvFn(s4, s5, 'conv4');
var conv5 = extractConvFn(s5, s6, 'conv5');
var conv6 = extractConvFn(s6, s7, 'conv6');
var conv7 = extractConvFn(s7, s8, 'conv7');
var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
if (getRemainingWeights().length !== 0) {
throw new Error("weights remaing after extract: " + getRemainingWeights().length);
}
......@@ -2285,12 +2348,7 @@
var bn = extractBatchNormParams(prefix + "/bn");
return { conv: conv, bn: bn };
}
function extractSeparableConvParams(prefix) {
var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
var bias = extractWeightEntry(prefix + "/bias", 1);
return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
}
var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
return {
extractConvParams: extractConvParams,
extractConvWithBatchNormParams: extractConvWithBatchNormParams,
......@@ -2363,7 +2421,7 @@
throw new Error('TinyYolov2 - load model before inference');
}
var out = tidy(function () {
var batchTensor = input.toBatchTensor(inputSize, false);
var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
batchTensor = _this.config.meanRgb
? normalize(batchTensor, _this.config.meanRgb)
: batchTensor;
......@@ -2394,7 +2452,7 @@
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)];
return [4 /*yield*/, toNetInput(input)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
case 2: return [2 /*return*/, _b.sent()];
}
......@@ -2416,7 +2474,7 @@
if (typeof inputSize !== 'number') {
throw new Error("TinyYolov2 - unknown inputSize: " + inputSize + ", expected number or one of xs | sm | md | lg");
}
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _b.sent();
return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
......@@ -2433,10 +2491,10 @@
boxes = results.map(function (res) { return res.box; });
scores = results.map(function (res) { return res.score; });
classScores = results.map(function (res) { return res.classScore; });
classNames = results.map(function (res) { return _this.config.classes[res.classLabel]; });
classNames = results.map(function (res) { return _this.config.classes[res.label]; });
indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
detections = indices.map(function (idx) {
return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx].toRect(), inputDimensions);
return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
});
return [2 /*return*/, detections];
}
......@@ -2451,7 +2509,12 @@
return loadQuantizedParams$1(modelUri, this.config.withSeparableConvs, defaultModelName);
};
TinyYolov2.prototype.extractParams = function (weights) {
return extractParams$1(weights, this.config.withSeparableConvs, this.boxEncodingSize);
var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;
var numFilters = filterSizes ? filterSizes.length : undefined;
if (numFilters !== 9) {
throw new Error("TinyYolov2 - expected 9 convolutional filters, but found " + numFilters + " filterSizes in config");
}
return extractParams$1(weights, this.config.withSeparableConvs, this.boxEncodingSize, filterSizes);
};
TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
var _this = this;
......@@ -2485,8 +2548,8 @@
var pos = { row: row, col: col, anchor: anchor };
var _b = this.withClassScores
? this.extractPredictedClass(classScoresTensor, pos)
: { classScore: 1, classLabel: 0 }, classScore = _b.classScore, classLabel = _b.classLabel;
results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, classLabel: classLabel }, pos));
: { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;
results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
}
}
}
......@@ -2500,9 +2563,9 @@
var row = pos.row, col = pos.col, anchor = pos.anchor;
return Array(this.config.classes.length).fill(0)
.map(function (_, i) { return classesTensor.get(row, col, anchor, i); })
.map(function (classScore, classLabel) { return ({
.map(function (classScore, label) { return ({
classScore: classScore,
classLabel: classLabel
label: label
}); })
.reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; });
};
......@@ -2690,9 +2753,9 @@
TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {
var _this = this;
groundTruth.forEach(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, classLabel = _a.classLabel;
if (typeof classLabel !== 'number' || classLabel < 0 || classLabel > (_this.config.classes.length - 1)) {
throw new Error("invalid ground truth data, expected classLabel to be a number in [0, " + (_this.config.classes.length - 1) + "]");
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {
throw new Error("invalid ground truth data, expected label to be a number in [0, " + (_this.config.classes.length - 1) + "]");
}
if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height }));
......@@ -2703,15 +2766,15 @@
var _this = this;
var groundTruthBoxes = groundTruth
.map(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, classLabel = _a.classLabel;
var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
return ({
box: (new Rect(x, y, width, height)).toBoundingBox(),
classLabel: classLabel
box: new Rect(x, y, width, height),
label: label
});
});
return groundTruthBoxes.map(function (_a) {
var box = _a.box, classLabel = _a.classLabel;
var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, width = _b.width, height = _b.height;
var box = _a.box, label = _a.label;
var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
var ctX = left + (width / 2);
var ctY = top + (height / 2);
var col = Math.floor((ctX / _this.inputSize) * _this.numCells);
......@@ -2721,7 +2784,7 @@
iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))
}); }).sort(function (a1, a2) { return a2.iou - a1.iou; });
var anchor = anchorsByIou[0].idx;
return { row: row, col: col, anchor: anchor, box: box, classLabel: classLabel };
return { row: row, col: col, anchor: anchor, box: box, label: label };
});
};
TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {
......@@ -2764,8 +2827,8 @@
var buf = mask.buffer();
var classValuesOffset = 5;
this.groundTruth.forEach(function (_a) {
var row = _a.row, col = _a.col, anchor = _a.anchor, classLabel = _a.classLabel;
buf.set(1, row, col, anchor, classValuesOffset + classLabel);
var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;
buf.set(1, row, col, anchor, classValuesOffset + label);
});
return mask;
};
......@@ -2878,11 +2941,15 @@
coordLoss: coordLoss.dataSync()[0],
classLoss: classLoss.dataSync()[0]
};
reportLosses(losses, filteredGroundTruthBoxes.length);
var report = {
losses: losses,
numBoxes: filteredGroundTruthBoxes.length,
inputSize: inputSize
};
reportLosses(report);
}
return totalLoss;
}, true);
netInput.dispose();
return [2 /*return*/, loss];
}
});
......@@ -2905,7 +2972,6 @@
return groundTruth.filter(function (_a) {
var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
var box = (new Rect(x, y, width, height))
.toBoundingBox()
.rescale({ height: imgHeight, width: imgWidth });
var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;
return !isTooTiny;
......@@ -2994,7 +3060,7 @@
throw new Error('FaceDetectionNet - load model before inference');
}
return tidy(function () {
var batchTensor = input.toBatchTensor(512, false);
var batchTensor = input.toBatchTensor(512, false).toFloat();
var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
var features = mobileNetV1(x, params.mobilenetv1);
var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
......@@ -3008,7 +3074,7 @@
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -3018,10 +3084,10 @@
if (minConfidence === void 0) { minConfidence = 0.8; }
if (maxResults === void 0) { maxResults = 100; }
return __awaiter$1(this, void 0, void 0, function () {
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, paddings, results;
var netInput, _a, _boxes, _scores, boxes, scores, i, scoresData, _b, _c, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
return __generator$1(this, function (_d) {
switch (_d.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _d.sent();
_a = this.forwardInput(netInput), _boxes = _a.boxes, _scores = _a.scores;
......@@ -3037,17 +3103,20 @@
scoresData = _c.apply(_b, [_d.sent()]);
iouThreshold = 0.5;
indices = nonMaxSuppression$2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
paddings = netInput.getRelativePaddings(0);
reshapedDims = netInput.getReshapedInputDimensions(0);
inputSize = netInput.inputSize;
padX = inputSize / reshapedDims.width;
padY = inputSize / reshapedDims.height;
results = indices
.map(function (idx) {
var _a = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
].map(function (val) { return val * paddings.y; }), top = _a[0], bottom = _a[1];
].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
var _b = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
].map(function (val) { return val * paddings.x; }), left = _b[0], right = _b[1];
].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
height: netInput.getInputHeight(0),
width: netInput.getInputWidth(0)
......@@ -3114,6 +3183,114 @@
};
}
var FaceLandmark68NetBase = /** @class */ (function (_super) {
__extends$1(FaceLandmark68NetBase, _super);
function FaceLandmark68NetBase(_name) {
var _this = _super.call(this, _name) || this;
_this.__name = _name;
return _this;
}
FaceLandmark68NetBase.prototype.runNet = function (_) {
throw new Error(this.__name + " - runNet not implemented");
};
FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
var inputDimensions = originalDimensions.map(function (_a) {
var width = _a.width, height = _a.height;
var scale = inputSize / Math.max(height, width);
return {
width: width * scale,
height: height * scale
};
});
var batchSize = inputDimensions.length;
return tidy(function () {
var createInterleavedTensor = function (fillX, fillY) {
return stack([
fill([68], fillX),
fill([68], fillY)
], 1).as2D(1, 136).as1D();
};
var getPadding = function (batchIdx, cond) {
var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
return cond(width, height) ? Math.abs(width - height) / 2 : 0;
};
var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
var landmarkTensors = output
.mul(fill([batchSize, 136], inputSize))
.sub(stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
})))
.div(stack(Array.from(Array(batchSize), function (_, batchIdx) {
return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
})));
return landmarkTensors;
});
};
FaceLandmark68NetBase.prototype.forwardInput = function (input) {
var _this = this;
return tidy(function () {
var out = _this.runNet(input);
return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
var height = _a[0], width = _a[1];
return ({ height: height, width: width });
}));
});
};
FaceLandmark68NetBase.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _a.sent();
landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return __generator$1(this, function (_c) {
switch (_c.label) {
case 0:
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? landmarksForBatch
: landmarksForBatch[0]];
}
});
});
};
return FaceLandmark68NetBase;
}(NeuralNetwork));
function fullyConnectedLayer(x, params) {
return tidy(function () {
return add(matMul(x, params.weights), params.bias);
......@@ -3174,18 +3351,18 @@
if (strides === void 0) { strides = [2, 2]; }
return maxPool(x, [2, 2], strides, 'valid');
}
var FaceLandmarkNet = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
return _super.call(this, 'FaceLandmarkNet') || this;
var FaceLandmark68Net = /** @class */ (function (_super) {
__extends$1(FaceLandmark68Net, _super);
function FaceLandmark68Net() {
return _super.call(this, 'FaceLandmark68Net') || this;
}
FaceLandmarkNet.prototype.forwardInput = function (input) {
FaceLandmark68Net.prototype.runNet = function (input) {
var params = this.params;
if (!params) {
throw new Error('FaceLandmarkNet - load model before inference');
throw new Error('FaceLandmark68Net - load model before inference');
}
return tidy(function () {
var batchTensor = input.toBatchTensor(128, true);
var batchTensor = input.toBatchTensor(128, true).toFloat();
var out = conv(batchTensor, params.conv0);
out = maxPool$1(out);
out = conv(out, params.conv1);
......@@ -3199,90 +3376,25 @@
out = maxPool$1(out, [1, 1]);
out = conv(out, params.conv7);
var fc0 = relu(fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc0));
var fc1 = fullyConnectedLayer(fc0, params.fc1);
var createInterleavedTensor = function (fillX, fillY) {
return stack([
fill([68], fillX),
fill([68], fillY)
], 1).as2D(1, 136).as1D();
};
/* shift coordinates back, to undo centered padding
x = ((x * widthAfterPadding) - shiftX) / width
y = ((y * heightAfterPadding) - shiftY) / height
*/
var landmarkTensors = fc1
.mul(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getPaddings(batchIdx).x + input.getInputWidth(batchIdx), input.getPaddings(batchIdx).y + input.getInputHeight(batchIdx));
})))
.sub(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(Math.floor(input.getPaddings(batchIdx).x / 2), Math.floor(input.getPaddings(batchIdx).y / 2));
})))
.div(stack(Array.from(Array(input.batchSize), function (_, batchIdx) {
return createInterleavedTensor(input.getInputWidth(batchIdx), input.getInputHeight(batchIdx));
})));
return landmarkTensors;
});
};
FaceLandmarkNet.prototype.forward = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
});
};
FaceLandmarkNet.prototype.detectLandmarks = function (input) {
return __awaiter$1(this, void 0, void 0, function () {
var _this = this;
var netInput, landmarkTensors, landmarksForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 1:
netInput = _a.sent();
landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
var landmarksArray, _a, _b, xCoords, yCoords;
return __generator$1(this, function (_c) {
switch (_c.label) {
case 0:
_b = (_a = Array).from;
return [4 /*yield*/, landmarkTensor.data()];
case 1:
landmarksArray = _b.apply(_a, [_c.sent()]);
xCoords = landmarksArray.filter(function (_, i) { return isEven(i); });
yCoords = landmarksArray.filter(function (_, i) { return !isEven(i); });
return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
height: netInput.getInputHeight(batchIdx),
width: netInput.getInputWidth(batchIdx),
})];
}
});
}); }))];
case 2:
landmarksForBatch = _a.sent();
landmarkTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, netInput.isBatchInput
? landmarksForBatch
: landmarksForBatch[0]];
}
});
return fullyConnectedLayer(fc0, params.fc1);
});
};
FaceLandmarkNet.prototype.loadQuantizedParams = function (uri) {
FaceLandmark68Net.prototype.loadQuantizedParams = function (uri) {
return loadQuantizedParams$2(uri);
};
FaceLandmarkNet.prototype.extractParams = function (weights) {
FaceLandmark68Net.prototype.extractParams = function (weights) {
return extractParams$2(weights);
};
return FaceLandmarkNet;
}(NeuralNetwork));
return FaceLandmark68Net;
}(FaceLandmark68NetBase));
var FaceLandmarkNet = /** @class */ (function (_super) {
__extends$1(FaceLandmarkNet, _super);
function FaceLandmarkNet() {
return _super !== null && _super.apply(this, arguments) || this;
}
return FaceLandmarkNet;
}(FaceLandmark68Net));
function createFaceLandmarkNet(weights) {
var net = new FaceLandmarkNet();
net.extractWeights(weights);
......@@ -3521,7 +3633,7 @@
throw new Error('FaceRecognitionNet - load model before inference');
}
return tidy(function () {
var batchTensor = input.toBatchTensor(150, true);
var batchTensor = input.toBatchTensor(150, true).toFloat();
var meanRgb = [122.782, 117.001, 104.298];
var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
var out = convDown(normalized, params.conv32_down);
......@@ -3552,7 +3664,7 @@
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true)];
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
}
});
......@@ -3564,7 +3676,7 @@
var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
return __generator$1(this, function (_a) {
switch (_a.label) {
case 0: return [4 /*yield*/, toNetInput(input, true)];
case 0: return [4 /*yield*/, toNetInput(input)];
case 1:
netInput = _a.sent();
faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
......@@ -3601,24 +3713,23 @@
function computeDescriptorsFactory(recognitionNet) {
return function (input, alignedFaceBoxes, useBatchProcessing) {
return __awaiter$1(this, void 0, void 0, function () {
var alignedFaceTensors, descriptors, _a;
var alignedFaceCanvases, descriptors, _a;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, extractFaceTensors(input, alignedFaceBoxes)];
case 0: return [4 /*yield*/, extractFaces(input, alignedFaceBoxes)];
case 1:
alignedFaceTensors = _b.sent();
alignedFaceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 3];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceTensors)];
return [4 /*yield*/, recognitionNet.computeFaceDescriptor(alignedFaceCanvases)];
case 2:
_a = _b.sent();
return [3 /*break*/, 5];
case 3: return [4 /*yield*/, Promise.all(alignedFaceTensors.map(function (faceTensor) { return recognitionNet.computeFaceDescriptor(faceTensor); }))];
case 3: return [4 /*yield*/, Promise.all(alignedFaceCanvases.map(function (canvas) { return recognitionNet.computeFaceDescriptor(canvas); }))];
case 4:
_a = _b.sent();
_b.label = 5;
case 5:
descriptors = _a;
alignedFaceTensors.forEach(function (t) { return t.dispose(); });
return [2 /*return*/, descriptors];
}
});
......@@ -3630,27 +3741,26 @@
return function (input, useBatchProcessing) {
if (useBatchProcessing === void 0) { useBatchProcessing = false; }
return __awaiter$1(this, void 0, void 0, function () {
var detections, faceTensors, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
var detections, faceCanvases, faceLandmarksByFace, _a, alignedFaceBoxes, descriptors;
return __generator$1(this, function (_b) {
switch (_b.label) {
case 0: return [4 /*yield*/, detectFaces(input)];
case 1:
detections = _b.sent();
return [4 /*yield*/, extractFaceTensors(input, detections)];
return [4 /*yield*/, extractFaces(input, detections)];
case 2:
faceTensors = _b.sent();
faceCanvases = _b.sent();
if (!useBatchProcessing) return [3 /*break*/, 4];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceTensors)];
return [4 /*yield*/, landmarkNet.detectLandmarks(faceCanvases)];
case 3:
_a = _b.sent();
return [3 /*break*/, 6];
case 4: return [4 /*yield*/, Promise.all(faceTensors.map(function (faceTensor) { return landmarkNet.detectLandmarks(faceTensor); }))];
case 4: return [4 /*yield*/, Promise.all(faceCanvases.map(function (canvas) { return landmarkNet.detectLandmarks(canvas); }))];
case 5:
_a = _b.sent();
_b.label = 6;
case 6:
faceLandmarksByFace = _a;
faceTensors.forEach(function (t) { return t.dispose(); });
alignedFaceBoxes = faceLandmarksByFace.map(function (landmarks, i) { return landmarks.align(detections[i].getBox()); });
return [4 /*yield*/, computeDescriptors(input, alignedFaceBoxes, useBatchProcessing)];
case 7:
......@@ -3896,6 +4006,14 @@
return scales;
}
var MtcnnBox = /** @class */ (function (_super) {
__extends$1(MtcnnBox, _super);
function MtcnnBox(left, top, right, bottom) {
return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
}
return MtcnnBox;
}(Box));
function normalize$1(x) {
return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); });
}
......@@ -3953,7 +4071,7 @@
var boundingBoxes = indices.map(function (idx) {
var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE$1) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE$1) / scale));
var score = scoresTensor.get(idx.y, idx.x);
var region = new BoundingBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
var region = new MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
return {
cell: cell,
score: score,
......@@ -4121,7 +4239,7 @@
indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7);
stats.stage2_nms = Date.now() - ts;
regions_1 = indicesNms.map(function (idx) {
return new BoundingBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
return new MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
});
finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
......@@ -4191,7 +4309,7 @@
var idx = _a.idx;
return idx;
});
filteredRegions = indices.map(function (idx) { return new BoundingBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
filteredBoxes = indices
.map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
filteredScores = indices.map(function (idx) { return scores[idx]; });
......@@ -4233,7 +4351,7 @@
Mtcnn.prototype.forwardInput = function (input, forwardParams) {
if (forwardParams === void 0) { forwardParams = {}; }
return __awaiter$1(this, void 0, void 0, function () {
var params, inputTensor, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
return __generator$1(this, function (_c) {
switch (_c.label) {
case 0:
......@@ -4241,7 +4359,6 @@
if (!params) {
throw new Error('Mtcnn - load model before inference');
}
inputTensor = input.inputs[0];
inputCanvas = input.canvases[0];
if (!inputCanvas) {
throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
......@@ -4249,12 +4366,11 @@
stats = {};
tsTotal = Date.now();
imgTensor = tidy(function () {
return bgrToRgbTensor(expandDims(inputTensor).toFloat());
return bgrToRgbTensor(expandDims(fromPixels(inputCanvas)).toFloat());
});
onReturn = function (results) {
// dispose tensors on return
imgTensor.dispose();
input.dispose();
stats.total = Date.now() - tsTotal;
return results;
};
......@@ -4313,7 +4429,7 @@
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)];
return [4 /*yield*/, toNetInput(input)];
case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
forwardParams])];
case 2: return [2 /*return*/, (_b.sent()).results];
......@@ -4329,7 +4445,7 @@
switch (_b.label) {
case 0:
_a = this.forwardInput;
return [4 /*yield*/, toNetInput(input, true, true)];
return [4 /*yield*/, toNetInput(input)];
case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
forwardParams])];
}
......@@ -4421,7 +4537,7 @@
}(TinyYolov2));
var detectionNet = new FaceDetectionNet();
var landmarkNet = new FaceLandmarkNet();
var landmarkNet = new FaceLandmark68Net();
var recognitionNet = new FaceRecognitionNet();
// nets need more specific names, to avoid ambiguity in future
// when alternative net implementations are provided
......@@ -4494,9 +4610,12 @@
}
exports.tf = tfCore_esm;
exports.Box = Box;
exports.BoundingBox = BoundingBox;
exports.LabeledBox = LabeledBox;
exports.ObjectDetection = ObjectDetection;
exports.Point = Point;
exports.PredictedBox = PredictedBox;
exports.Rect = Rect;
exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
exports.extractWeightEntryFactory = extractWeightEntryFactory;
......@@ -4538,6 +4657,9 @@
exports.isDimensions = isDimensions;
exports.computeReshapedDimensions = computeReshapedDimensions;
exports.getCenterPoint = getCenterPoint;
exports.range = range$1;
exports.isValidNumber = isValidNumber;
exports.isValidProbablitiy = isValidProbablitiy;
exports.NeuralNetwork = NeuralNetwork;
exports.FaceDetection = FaceDetection;
exports.FaceLandmarks = FaceLandmarks;
......@@ -4552,9 +4674,10 @@
exports.createFaceDetectionNet = createFaceDetectionNet;
exports.faceDetectionNet = faceDetectionNet;
exports.FaceDetectionNet = FaceDetectionNet;
exports.FaceLandmarkNet = FaceLandmarkNet;
exports.createFaceLandmarkNet = createFaceLandmarkNet;
exports.faceLandmarkNet = faceLandmarkNet;
exports.FaceLandmarkNet = FaceLandmarkNet;
exports.FaceLandmark68Net = FaceLandmark68Net;
exports.createFaceRecognitionNet = createFaceRecognitionNet;
exports.faceRecognitionNet = faceRecognitionNet;
exports.FaceRecognitionNet = FaceRecognitionNet;
......
dist/face-api.js.map
This source diff could not be displayed because it is too large. You can view the blob instead.
dist/face-api.min.js
This source diff could not be displayed because it is too large. You can view the blob instead.
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment